CN105232054A - Human body endocrine system health risk early warning system - Google Patents

Abstract

The invention provides a human body endocrine system health risk early warning system, which comprises a human body health scanning system, an endocrine system health risk early warning data processor and an endocrine system health risk early warning result display, wherein the human body health scanning system is used for scanning and evaluating the function values of each tissue and organ of a human body endocrine system; the endocrine system health risk early warning data processor uses a human body endocrine system health risk early warning model for performing human body endocrine system health risk early warning; the output end of the human body health scanning system is connected with the input end of the endocrine system health risk early warning data processor; and the output end of the endocrine system health risk early warning data processor is connected with the endocrine system health risk early warning result display. The organ function of the endocrine system is comprehensively scanned in a short time; the organ function of the endocrine system is evaluated; and the human body tissue energy change and the organ functional change are discovered in time. The potential risk factors and the disease development direction are predicted through combining endocrine system clinical index data, and the health risk of the endocrine system realizes early-stage early warning.

Description

A kind of human endocrine system health Warning System

Technical field

The invention belongs to subhealth state Risk-warning technical field, be specifically related to a kind of human endocrine system health Warning System.

Background technology

Along with the development of China's economy, in crowd, the ratio of subhealth state is increasing year by year, and about have 700,000,000 populations to be in sub-health state, enterprise's white collar, government functionaries are due to its job specification, be one of main population of subhealth state, the health of Cadres of Enterprises also allows of no optimist.Thus carry out the research of the health risk assessment early warning technology aspect to such crowd, realize the early warning to such population health situation, early intervention very necessary.

A global investigation of WHO shows, the people of true health only accounts for 5%, and the people suffering from disease accounts for 20%, and the people of 75% is in healthy low-quality states.Healthy low-quality states is also known as sub-health state (also claiming the third state, gray states, sick front state, Patients with Subclinical, preclinical phase, stadium of diving etc.), now human body feels slight without clinical symptoms or symptom, not yet there is obvious change in general clinical examination index, but health has potential pathological information.The formation of disease absolutely not overnight, experienced by and change from cellular energy---the process of tissue energy change---organ dysfunction sexually revises---organ generation pathological changes, most people is difficult to discover to a series of changes before organ generation pathological changes, only have and run up to a certain degree when organ dysfunction sexually revises, organ generation pathological changes, at this moment people just can feel sick.Thus, the some diseases of health is the process that experienced by---subhealth state---morbidity from health.When health is in tissue energy change and organ dysfunction sexually revises the stage, the health of people is just in sub-health state.If now people can the health risk that exists of Timeliness coverage health, and take Health intervention measure, just can prevent organ generation pathological changes.At present, general health check-ups means can only find that obvious damage has appearred in the histoorgan generation pathological changes of human body or histoorgan, and can not the tissue energy in Timeliness coverage human-body sub-health stage change and organ dysfunction situation about sexually revising.Therefore, health risk early warning technology, and have its very important meaning based on the Health intervention on health risk early warning technology basis to prophylactic!

Health risk early warning technology is exactly use the advanced full-automatic body scanning technique means of human body to carry out complete detection to human body, carrying out on assessment basis to human body each system major organs is functional, in conjunction with preclinical medicine, clinical medicine, preventive medicine, Chinese medicine knowledge, early warning is carried out to eight Iarge-scale system health risks of human body.Apply this technology and can carry out early stage Health intervention targetedly to the people being in sub-health state, prevent organ generation pathological changes.At present, people carry out general health check-ups and still do not break away from by finding that disease is to the Diagnosis-treat Model of disease therapy.This Diagnosis-treat Model can only be accomplished " ill cure the disease ", cannot realize the object of " anosis diseases prevention ".General health check-ups project only checks for some organ of human body.Meanwhile, be also subject to the limitation impact of physician specialty know-how and examination thinking, the health status of system, all sidedly each organ of examination human body and system cannot be accomplished, also really cannot realize early discovery, the early intervention of health abnormal information.Moreover general health check-ups often use x-ray, intervention means, usually bring extra damage to person under inspection.This health check-ups mode is not only runed counter to modern healthy Overall View and Non-Invasive requirement, and may cause potential radiation damage and iatrogenic infection.

Domestic and international Epidemiology: according to statistics, the U.S. has that 6,000,000 people are under a cloud is in sub-health state every year, and the age is many between 20 ~ 45 years old.Have the adult male of 14% and 20% women performance have obvious fatigue, wherein 1/8 develops into the chronic fatigue syndrome existed in sub-health state.The subhealth state problem of China is also quite severe, and have data to show, China about has 700,000,000 populations to be in sub-health state, and government functionaries is due to its job specification, and be one of main population of subhealth state, the health of Cadres of Enterprises also allows of no optimist.

When " sub-health state " appears in human endocrine system, there will be the disorder of human hormone's level, easily show as the problem such as insomnia, forgetful, dizzy, anemia, obesity, climacteric syndrome, sexual hypofunction.

The people such as the Liu Baoyan of China Academy of TCM design the basic Characteristics of Syndromes questionnaire of the sub-health state traditional Chinese medical science, comprise 6 parts such as Physical condition, weather, feelings will situation, energy situation, natural endowment situation, social environment situation, totally 124 issue entry, adopt 5 grades of scorings, the score value of each entry assigns to 5 points from 1, and meaning is by well to bad.This questionnaire is comprehensively full and accurate, can judge the health status of crowd to a certain extent, discloses the regularity of distribution of Sub-health State in TCM syndrome.The people such as Wang Xueliang on this basis, have developed sub-health state TCM syndrome's questionnaire, and comprise somatization, mental symptoms, social symptom 3 aspects, totally 72 entries, content simplifies greatly, are easy to operation.

The CMI questionnaire content of Cornell Univ USA's establishment comprises 4 parts: somatization, family history and history of past illness, general health and custom, mental symptom.Be divided into 18 parts, totally 195 entries.Each entry is answered "Yes" person and is remembered 1 point; Answer "No" person and remember 0 point, whole entry is added the total score drawing CMI.Wherein, there are 51 entries to be problems of emotion, emotion and the behavior aspect relevant with ergasia, are called MR part.CMI further defines examination standard, is: male total score >=935, M-R >=15 point in the examination standard of China; Women total score >=40 point, M-R >=20 point.What reach this standard is the body and mental maladjustment person that examination arrives.This scale is combined with self-control scale by Zhou Lingling etc., has carried out subhealth state investigation to 372 teachers in primary and middle schools, finds that teachers in primary and middle schools' subhealth state incidence rate is 55.11%.

A lot of scholar carries out quantitative study by pandemic MDI health evaluating method to sub-health state, it was that WHO is for human death, the indices endangered suggested by maximum disease measures originally, give a mark item by item according to the actual detected status of measured and (take hundred-mark system, full marks are 100 points), health corresponding to WHO defines, carry out overall merit, its standard is: more than 85 points is health status, less than 70 points is morbid state, and 70 ~ 85 are divided into sub-health state (third state).The prompting of MDI institute foundation comprise be arranged in order the corporality indexs such as detection, effect of taking medicine detection are damaged to cardiovascular and cerebrovascular disease monitoring and apoplexy forecast, the prompting of malignant tumor sign, organ disease prompting, blood and anaphylactic disease prompting, internal pollution mensuration, hormonal system inspection, limbs, and the psychology increased in recent years, human communication disorders index MDI health evaluating scale.

Machine learning is the core research direction of current large data age, and the achievement in research of machine learning is widely applied in the middle of the fields such as pattern recognition, computer vision, data mining, cybernetics, and in the middle of the every aspect penetrating into people's daily life.And in the middle of the research of machine learning, the research of grader is in occupation of very important status, the practical problem of the overwhelming majority can convert a classification problem to, and the performance of grader is application achievements whether key often.The great potential excavating grader (as support vector machine (SVM), extreme learning machine (ELM) etc.) has become the mainstream research direction of current machine learning.

Extreme learning machine develops from the neutral net of single hidden layer, and have and be easy to realize, and speed is fast, the features such as generalization ability is strong, and becomes the object of study of numerous scholars.Single hidden layer Feedback Neural Network has two relatively more outstanding abilities: (1) directly can simulate complicated mapping function from training sample, and (2) can be difficult in a large number supply a model with the nature of traditional classification parametric technique process or manual site.But single hidden layer Feedback Neural Network lacks learning method more fast.The each iteration of error backpropagation algorithm needs to upgrade a lot of values, and the time spent is well below the tolerated time.Often can see for training a single hidden layer Feedback Neural Network to take a few hours, a couple of days or more time.Extreme learning machine is widely applied when carrying out classification prediction in a lot of field.

Statistical Learning Theory is based upon on structural risk minimization basis, and it is a set of new theoretical system set up for the Machine Learning Problems under Small Sample Size specially.The algorithm of support vector machine of Corpus--based Method theory of learning has the advantages such as theoretical complete, global optimization, strong adaptability, Generalization Ability are good, is the new focus that machine learning is studied.It is while minimizing empiric risk, effectively improves the generalization ability of algorithm, has good using value and development prospect.

Machine learning based on data is the importance in modern intellectual technology, research is from observed data sample set off in search rule, these rules are utilized to predict the data that Future Data maybe cannot be observed, the important foundation comprising the existing machine learning method of pattern recognition, neutral net etc. is traditional statistics, prerequisite has abundant sample, namely traditional statistics research is the asymptotic theory of number of samples when being tending towards infinity, is difficult in limited time obtain desirable effect when number of samples has.Statistical Learning Theory (the StatisticalLearningTheory of Vapnik, SLT) statistical law under Small Sample Size and learning method character is then proposed emphatically, for Machine Learning Problems establishes a good theoretical frame, and developed a kind of general learning method newly---support vector machine (SupportVectorMachine, SVM) thus.

Sample vector, by introducing kernel function, is mapped to high-dimensional feature space, then in higher dimensional space, constructs optimal classification surface by SVM, obtains linear optimal decision function.SVM can carry out the over-fitting of inhibition function by the gap metric controlling hyperplane; By adopting, kernel function is ingenious solves problem of dimension, avoids the directly related of learning algorithm computation complexity and sample dimension; Also due to the use of SRM principle, SVM is provided with good Generalization Ability.

The feature of support vector machine

1. in system structure simple surfaces support vector machine similar with in three_layer planar waveguide, but they have basic difference.Support vector machine structure is very simple, does not need too much priori.Its hidden layer is determined automatically by algorithm, can with the needs of practical problem adaptive adjustment scale and size, there is not the structure choice problem of similar neutral net.And the hidden layer number of neutral net and the interstitial content of every layer determine all in advance, in the algorithm of neutral net, only automatically produce network weight.

2. Global Optimality support vector machine is by solving optimal hyperlane to learn, the Nonlinear Classification face in the corresponding raw mode space of the hyperplane in high-dimensional feature space.Finding the problem of optimal hyperlane is utilize Lagrange optimization method to be converted into quadratic programming problem, and what can ensure that algorithm of support vector machine obtains is globally optimal solution, makes it become a kind of outstanding learning algorithm.In neutral net, the possibility of result obtained is locally optimal solution.Particularly when the dimension of training sample is higher, may there is many local extremums in higher dimensional space, and have larger difference between different local extremums, and the training and testing result of neutral net can present larger randomness.

3. Generalization Ability strong support vector machine Corpus--based Method theory of learning, adopt structural risk minimization, can do reasonably compromise between empiric risk and model complexity, the decision rules obtained by limited training sample the popularization performance of learning machine can be improved, even if still can obtain less error to independently test set as far as possible.Neutral net have employed and keeps fiducial range and the strategy minimizing empiric risk, but clear and definite foundation instructs how structural learning machine makes fiducial range minimum; And SVM adopt maintenance empiric risk is fixed and minimizes the method for fiducial range.From the angle obtaining good Generalization Ability, SVM is obviously much brilliant than neutral net.

Extreme learning machine (ELM) and support vector machine (SVM) comparative analysis:

Extreme learning machine ELM is that one is simple and easy to use, effective Single hidden layer feedforward neural networks SLFNs learning algorithm.Traditional Learning Algorithm (as BP algorithm) needs artificially to arrange a large amount of network training parameters, and is easy to produce locally optimal solution.Extreme learning machine only needs the hidden node number arranging network, does not need to adjust the biased of the input weights of network and hidden unit, and produce unique optimal solution in algorithm implementation, therefore has the fast and advantage that Generalization Capability is good of pace of learning.Extreme learning machine is incorporated in the prediction of reservoir permeability herein, by contrast support vector machine, analyze its reservoir permeability prediction in feasibility and advantage.Experimental result shows, extreme learning machine and support vector machine have approximate precision of prediction, but has obvious advantage at Selecting parameter and pace of learning limit superior learning machine.

To support vector machine (SupportVectorMachine, be called for short SVM), in learning process, need artificially to arrange the parameters such as kernel function, error control parameter and penalty coefficient, parameter determines difficulty, and needs the consumption plenty of time to carry out parameter adjustment.Extreme learning machine (ExtremeLearningMachine, be called for short ELM) as a kind of Novel learning algorithm of Single hidden layer feedforward neural networks, only need the hidden node number that network is set, do not need to adjust the input weights of network and the biased of hidden unit in algorithm implementation, and produce unique optimal solution.Therefore, have that Selecting parameter is easy, the fast and advantage that Generalization Capability is good of pace of learning.

The research of subhealth state also also exists many problems at present, and relatively more outstanding has following 3 points: (1) cause of disease is not yet reached common understanding.Such as chronic fatigue syndrome is the one of sub-health state, and someone thinks that its origin cause of formation is that viral infection causes, and also someone thinks what immune system dysfunction caused.(2) unified judgment criteria is lacked.About sub-health state, worldwide not yet form unified judgment criteria.(3) therapeutic effect is difficult to assessment.The performance of subhealth state is varied, and belongs to symptomatic treatment to the treatment of subhealth state more, brings difficulty to therapeutic evaluation.

Summary of the invention

For prior art Problems existing, the invention provides a kind of human endocrine system health Warning System.

Technical scheme of the present invention is as follows:

A kind of human endocrine system health Warning System, comprising:

Health scanning system: respectively organize the function value with organ for scanning and assess human body hormonal system, comprise the function value of hypophysis region, hypothalamic areas, vassopressin, aldosterone, testosterone;

Hormonal system health risk warning data processor: the clinical indices data obtaining hormonal system, utilize human endocrine system health risk warning model, respectively organize based on human endocrine system and carry out human endocrine system health Risk-warning with the clinical indices data of the function value of organ and hormonal system; The human endocrine system that is input as of described human endocrine system health risk warning model respectively organizes the clinical indices data with the function value of organ and hormonal system, exports as hormonal system health risk early warning result; The clinical indices data of described hormonal system comprise: hormonal system medical history, body are looked into, lab index; Described hormonal system health risk early warning result comprises: hormonal system health status, thyroid are hyperfunction, thyroid goes down, hyperpituitarism, hypophysis go down;

Hormonal system health risk early warning result display: the hormonal system health risk early warning result that display hormonal system health risk warning data processor exports;

The outfan of health scanning system connects the input of hormonal system health risk warning data processor, and the outfan of hormonal system health risk warning data processor connects hormonal system health risk early warning result display.

Described hormonal system health risk warning data processor comprises:

Data acquisition unit: the human endocrine system gathering the scanning of health scanning system and assessment respectively organizes the clinical indices data with the function value of organ, hormonal system;

Human endocrine system health risk warning model sets up unit: the human endocrine system according to scanning and assessment is respectively organized and the function value historical data of organ and the clinical indices historical data of hormonal system and corresponding hormonal system health risk history early warning result training human body hormonal system health risk Early-warning Model;

Human endocrine system health Risk-warning unit: utilize human endocrine system health risk warning model, the clinical indices data with the function value of organ, hormonal system are respectively organized based on the current human endocrine system collected, carry out human endocrine system health Risk-warning, obtain human endocrine system health Risk-warning result, export hormonal system health risk early warning result display to.

Described human endocrine system health risk warning model is set up unit and is comprised:

Sample generation module: respectively organize according to the human endocrine system of scanning and assessment and generate sample set with the function value historical data of organ and the clinical indices historical data of hormonal system and corresponding hormonal system health risk history early warning result, using a part of sample in sample set as training sample, all the other are as test sample book;

Model training module: using the training sample in sample set as input, using hormonal system health risk history early warning result as output, adopt extreme learning machine ELM and support vector machines model respectively, carry out the training of human endocrine system health risk warning model, training obtains human endocrine system health risk warning model;

Model measurement module: utilize the test sample book in sample set, tests the training result of the human endocrine system health risk warning model that the human endocrine system health risk warning model adopting extreme learning machine ELM to train and employing support vector machines model training go out respectively;

Model selection module: select test result to evaluate height and the high human endocrine system health risk warning model of test result accuracy rate as final human endocrine system health risk warning model.

Beneficial effect:

The present invention utilizes whole body health scanning system, as a kind of health detection equipment of quick, painless, noinvasive, low cost, scanning comprehensively can be carried out to human body hormonal system major organs function at short notice and just can obtain individual health and fitness information, assess human endocrine system major organs is functional, the tissue energy of Timeliness coverage human body changes and organ dysfunction sexually revises situation.And by the clinical indices data in conjunction with hormonal system, predict potential risk factor and disease progression direction, early warning is carried out to the hormonal system health risk of human body.Meanwhile, use health risk early warning result to carry out Health intervention targetedly to the people being in sub-health state, realize the object of " anosis diseases prevention ".

The present invention breaches traditional health check-up pattern, by full-automatic whole body health scanning system equipment for rely on hurtless measure examination based on, in conjunction with preclinical medicine, clinical medicine, preventive medicine, Chinese medicine knowledge, set up human endocrine system health Warning System.Meanwhile, this early warning result be can be applicable to Health intervention.Explore the data base setting up a set of comprehensive health intervening measure based on environmental medicine, sports medical science, Chinese medicine, psychology, threpsology, health science, the health risk early warning and Health intervention management system that are applicable to China inward and outward personnel are set up in exploitation.Further, also progressively can be applicable in enterprises and institutions of China worker, community resident.Really realize the early discovery of health abnormal information, the object of early intervention, reach and reduce ailing, slow down aging, reduce medical expense, alleviate the effect of government burden.

The present invention can to the personnel of the diplomat of government offices, international cooperation scientific research project, United Nations Peacekeeping Force of China soldier, and the health of the high-grade, precision and advanced entry and exit such as the project program abroad technical staff talent carries out the health control of long-term normalization, realize these talent's health status risk early warnings, and pass through comprehensive health intervening measure and the tracking in time guidance of science, them are made to keep a good condition of health and abundant energy, the high efficiency work completing them, for China contributes to the development of external-open.

Disease control association of U.S. achievement in research shows: the individual of 90% and enterprise are by health control, and medical expense can reduce 90%; And the individual of 10% and enterprise do not participate in health control, medical expense then increases by 90%.Visible enforcement of the present invention and apply and can greatly raise labour productivity, Economy type medicine expense, increases the performance of enterprises.

The ratio that chronic diseases in China death toll accounts for total death toll by 1993 73.8% rise to the Chinese chronic disease death tolls in 80.9%, 2005 of 2000 up to 7,500,000.System of the present invention can find the risk of human body as early as possible, just controls health risk by Health intervention means and further develops, reach restorative object when making people also be in the state of subhealth state.This will reduce the speed of China resident disease prevalence rising greatly.Reduce people simultaneously and suffer ailing torment, reduce the expense of seeing a doctor, curing the disease, all burden is alleviated to individual, family, unit and even entire society.

Accompanying drawing explanation

Fig. 1 is the human endocrine system health Warning System block diagram of the specific embodiment of the invention;

Fig. 2 is the hormonal system health risk warning data processor block diagram of the specific embodiment of the invention;

Fig. 3 is that the human endocrine system health Warning System that utilizes of the specific embodiment of the invention carries out the flow chart of human endocrine system health Risk-warning.

Detailed description of the invention

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is elaborated.

The health scanning system data of inward and outward personnel and clinical indices data during present embodiment gathers year June in January, 2012 to 2013, sample size is 20,000 parts, and collecting location is Huhehaote City, Siping City, Shenyang City, Zhangjiakou City, Wuhan City.The age bracket of crowd is: 18 years old ~ 70 years old, male's quantity 8000 example, and women's quantity is 12000 examples, and M-F is 4:5.

Health scanning system adopts eagle to drill whole body health scanning system, full name is DDFAO whole body health scanning system, it adopts low-voltage DC boost inductor technology, forehead, hands, foot symmetry places 6 electrodes, 22 tagmas continue to send the self adaptation of on average every 3 seconds 255 times, automatic adjustment low-voltage (1.28V) DC signal, the signal of telecommunication is converted into ion flow in tissue, according to ion flow at the moon, the polarization motion at sun the two poles of the earth obtains the resistance through tissue, electrical conductivity, pH value, voltage and pass the action potential of cell membrane, the electrophysiologic activity of the Interstitial cell of each internal organs of human activin.And according to the unidirectional general character of physiological feedback signal, carry out instant amperometry analysis, gather the information of bodily fuctions in digitized form, by mathematical model, 3D reconstruction is carried out to data.Eagle is drilled by scanning in 3 ~ 5 minutes, just can compare comprehensive functional status and assess each digestion tissue of whole body, each organ.Application quantum physics, neuro physiology, function of nervous system are learned, the multi-door subjects such as modern medicine, clinical medicine, mathematics, statistics, computer technology, are at present in the world for the model of preventive medicine.The assessment that eagle drills system on human body subhealth state needs to be based upon on the Information base of a large amount of crowd, need to gather the bulk information storehouse of country variant, various physiology that different ethnic group detects, pathological index, and also have for the data message of asian population and huge improve space.

In present embodiment, human endocrine system health Warning System, as shown in Figure 1, comprising:

Health scanning system: respectively organize the function value with organ for scanning and assess human body hormonal system, comprise the function value of hypophysis region, hypothalamic areas, vassopressin, aldosterone, testosterone;

The human endocrine system that eagle drills the scanning of whole body health scanning system respectively organizes the function value criterion with organ, comprising: the function value <-20 of the function value <-20 in hypophysis region, the function value <-20 of hypothalamic areas, vassopressin or > 20, the function value > 20 of aldosterone, the function value < 20 of testosterone;

Meet any one function value criterion above and be hormonal system extremely, there is health risk in hormonal system, is labeled as 1, otherwise be labeled as 0.

Hormonal system health risk warning data processor: the clinical indices data obtaining hormonal system, utilize human endocrine system health risk warning model, respectively organize based on human endocrine system and carry out human endocrine system health Risk-warning with the clinical indices data of the function value of organ and hormonal system; The human endocrine system that is input as of described human endocrine system health risk warning model respectively organizes the clinical indices data with the function value of organ and hormonal system, exports as hormonal system health risk early warning result; The clinical indices data of described hormonal system comprise: hormonal system medical history, body are looked into, lab index; Described hormonal system health risk early warning result comprises: hormonal system health status, thyroid are hyperfunction, thyroid goes down, hyperpituitarism, hypophysis go down;

Hormonal system health risk warning data processor comprises:

Data acquisition unit: the human endocrine system gathering the scanning of health scanning system and assessment respectively organizes the clinical indices data with the function value of organ, hormonal system;

Human endocrine system health risk warning model sets up unit: the human endocrine system according to scanning and assessment is respectively organized and the function value historical data of organ and the clinical indices historical data of hormonal system and corresponding hormonal system health risk history early warning result training human body hormonal system health risk Early-warning Model;

Human endocrine system health risk warning model is set up unit and is comprised:

Sample generation module: respectively organize according to the human endocrine system of scanning and assessment and generate sample set with the function value historical data of organ and the clinical indices historical data of hormonal system and corresponding hormonal system health risk history early warning result, using a part of sample in sample set as training sample, all the other are as test sample book; Totally 11046 samples in sample set, using the sample of 7000 in sample set as training sample, remaining 4046 samples are as test sample book; Each sample packages drills the scanning index of whole body health scanning system containing eagle, clinical indices, assessment result amount to 433 information.

Hormonal system health risk Alert Standard is as follows:

(1) clinical indices is as follows:

1, medical history: diabetes medical history, gynecological endocrine disease or thyroid disease history

2, the sings and symptoms looked into of body: women: (1) skin worsens: skin has occurred suddenly the obfuscation of a lot of macula lutea, complexion, mottle; (2) short-tempered; (3) gynecological endocrine disease: endometriosis, menstrual blood volume are irregular, dysmenorrhea, menoxenia etc.; (4) fat; (5) infertile; (6) cystic hyperplasia of breast: cyclomastopathy and breast carcinoma; (7) chaeta is too much; (8), poliosis, senilism; (9), tinnitus; Male: (1) is long pox on the face; (2) testicular function is low; (3) adrenal gland diseases: the diseases such as Addison's disease (adrenocortical insufficiency), cushing's syndrome (hypercortisolism), womanlike adrenal cortical tumor, congenital adrenal hyperplasia, aldosteronism, all can cause male sterility; (4) thyroid disease: hypothyroidism or hyperthyroidism, gynaecomastia, hyposexuality, sexual impotence etc.; (5), can there is nympholepsy, build change etc. in early days in hypophysis pathological changes: hyperpituitarism, then hyposexuality, dysspermatism, sexual impotence etc. just occur and cause sterile.Primary pituitary hypofunction, as pituitary tumor, inflammation, surgical injury or radiation destroy hypophysis, causes primary pituitary hypofunction, occurs that libido, sexual potency reduce, atrophy of testis.

3, lab index

(1) pathoglycemia;

(2) testosterone, estrogen (estradiol), progestogen, follicule-stimulating hormone (FSH), lutropin, lactotropin, aldosterone, insulin, thyroxine, auxin are extremely.

(2) eagle drills the directive function value of whole body health scanning system

Hypophyseal area domain-functionalities value <-20, hypothalamic areas function value <-20, vassopressin function value <-20 or > 20, aldosterone function value > 20, Function of Testosterone value < 20

If meet above-mentioned all criterion, then hormonal system health risk is judged to be 1, and namely hormonal system exists health risk, otherwise hormonal system judges to be in health status.

Each organ health risk early warning of hormonal system: thyroid, hypophysis

(1) thyroid Risk-warning standard:

The hyperfunction Risk-warning standard of thyroid:

1, clinical indices

1) the hyperfunction medical history of thyroid;

2) sings and symptoms: thyromegaly, bulimia, lose weight, tachycardia, emotion are easy to exciting, To Be Protected from Heat hyperhidrosis, hand shaking, expophthalmos etc.;

3) clinical examination FT4 >=25.7pmol/L, FT3 >=11.4pmol/L, TSH reduce (diffuse goiter accompanies the diseases such as hyperthyroidism);

2, eagle drills the directive function value of whole body health scanning system

(1) right lobe of thyroid gland regional function value >20, left lobe of thyroid gland regional function value >20; (2) the thyroxin value >0 of interstitial;

Meet arbitrary Risk-warning standard in above clinical indices simultaneously and meet eagle and drill arbitrary Risk-warning standard in the directive function value of whole body health scanning system, then the hyperfunction risk of thyroid is judged to be 1, there is the hyperfunction risk of thyroid.

Thyroid goes down Risk-warning standard:

1, clinical indices

1) hypothyroidism medical history;

2) sings and symptoms: 1. pale complexion, eyelid and buccal edema due to deficiency, apathetic expression, dull-witted, the whole skin universe is dry, thicken, coarse many desquamations, nonpitting edema, alopecia, and the trick palm is in chlorotic color, and body weight increases, the thick and embrittlement of a few patients fingernail.2. neuropsychiatric: hypomnesis, mental retardation, drowsiness, bradykinesia, worries too much, dizzy, headache, tinnitus, and deaf, nystagmus, ataxia, tendon reflex is blunt, and Achilles tendon reflex relaxation period time lengthening, severe one can occur dementia, numb, even lethargy.3. cardiovascular system: bradycardia, cardiac output reduces, and blood pressure is low, and hear sounds is low blunt, cardiac dilatation, can and premature coronary heart disease, but generally there is not angina pectoris and heart failure, sometimes can with pericardial effusion and hydrothorax.There is myxedema cardiomyopathy in serious symptom person.4. digestive system: anorexia, abdominal distention, constipation.Can paralytic ileus be there is in severe one.Gallbladder contraction weakens and swells, and half patient has achlorhydria, causes pernicious anemia and iron deficiency anemia.5. motor system: muscle weakness is unable, pain, tetanic, can with arthropathy as chronic arthritis.6. hormonal system: feminine menstrual is too much, prolonged illness amenorrhea, infertility; Impotence, hyposexuality.There is lactogenic in a few patients, Secondary cases hypophysis increases.7. when being in a bad way, due to myxedema coma or title " hypothyroidism crisis " stress be brought out by cold, infection, operation, anesthesia or tranquilizer misapplication etc.Show as hypothermia (T < 35 DEG C), breathing is slowed down, bradycardia, blood pressure drops, and extremity muscular strength relaxes, and hyporeflexia or disappearance, even go into a coma, shock, cardiorenal function exhaustion.8. cretinism: have a dull expression on one's face, pronounce low and hoarse, face is pale, all edema of socket of the eye, and two eye distances are broadening, and the bridge of the nose is flat to collapse, the thick sialorrhea of lip, the overhanging extremity tubbiness of swelling of the tongue, duck step.

3) clinical examination

1. serum T T4, TT3, FT4, FT3 are lower than normal value

2. serum TSH value

(1) hypothyroidism disease TSH obviously raises and accompanies free T4 to decline simultaneously.Subclinical hypothyroidism disease serum T T4, TT3 value can be normal, and serum TSH slightly raises, and serum TSH level, after the test of TRH analeptic, reacts higher than normal person.

(2) nanosom hypothyroidism disease serum TSH level is low or normal or higher than normally, reactionless to TRH stimulation test.After application TSH, serum T T4 level raises.

(3) hypothalamic hypothyroidism disease serum TSH level is low or normal, good to the reaction of TRH stimulation test.

(4) peripheral hypothyroidism (thyroid hormone resistance syndrome) central resistant TSH raises, and surrounding tissue resistant TSH is low, and systemic resistance person TSH has different manifestations.

3.X ray examination: cardiac dilatation, heartbeat slows down, and pericardial effusion, skull plain film show that sella turcica can increase.

4. Electrocardioscopy: show low-voltage, Q-T interval prolongation, ST-T is abnormal.Ultrasonic cardiography diagram cardiac muscle thickens, pericardial effusion.

5. blood fat, creatine phosphokinase activity increase, and glucose tolerance curve is low flat.

2, eagle drills the directive function value of whole body health scanning system

(1) right lobe of thyroid gland regional function value <-20, left lobe of thyroid gland regional function value <-20

(2) thyroxin value≤-20 of interstitial, interstitial thyrotropin function value <0

Meet arbitrary Risk-warning standard and eagle in above clinical indices simultaneously and drill arbitrary Risk-warning standard in the directive function value of whole body health scanning system, then the thyroid risk that goes down is judged to be 1, there is thyroid and to go down risk.

(2) hypophysis Risk-warning standard:

Hypopituitarism Risk-warning standard:

1, clinical indices;

I, medical history and sign: central diabetes insipidus, defect of visual field, Fundus oculi changes, visual disorder, drowsiness, tachycardia, cardiopalmus, cortical hypofunction, blood pressure drops, loss of consciousness, arthralgia, amyotrophy, the swollen discomfort of hip acid, intermittent claudication, polyuria, polydipsia, cachexia, weak, diplopia, hypotension, inappetence, unable, cyanosis, palor, tired, urine collapses, hypercortisolemic, tired, hemianopsia, galactorrhea

II, iconography (X-ray, CT, magnetic resonance) localization examination

III, Intervention around

1. twenty-four-hour urine 17-KS (17-KS), 17OHS (17-OHCS) and UFC are all lower than normal value.

2. ACTH stimulation test: ACTH25 is dissolved in 5% Fructus Vitis viniferae glucose saline 500ml, quiet, maintains 8 hours, and this patient is in delayed response, and namely need continuously quiet after 2 ~ 3 days, urine 17-KS and 17-OHCS just raises gradually.

IV, thyroid function

1. serum T 3, T4 and Thyroid Intaking I ¹³¹rate is lower than normally.

2. TSH stimulation test TSH10 intramuscular injection, once-a-day, totally 3 days.This patient first shape I ¹³¹gland takes the photograph rate and serum T 3, T4 can have and increases, but shows, in delayed response not as normal person.

V, gonad function

Male's serum testosterone, urine 17-KS; Women's serum estradiol and urine estrogen (estrone, estradiol, estriol) low SI.Vaginal cytology smear examination shows estrogen activity and to go down.

VI, antepituitary function

(1) serum TSH, LH, FSH, ACTH and GH can lower than normal values.

(2) ACTH secretion test

(3) growth hormone (GH) secretion test

(4) lactotropin (PRL) secretion test

(5) promoting sexual gland hormone (Gn) secretion test

(6) thyrotropin (TSH) secretion test

2, eagle drills the directive function value of whole body health scanning system:

Hypophyseal area domain-functionalities value≤-20, function value≤-20, hypothalamic areas, vassopressin function value≤-20;

Adrenal gland: the regional function value of right adrenal gland cortex or regional function value≤-20 of left adrenal gland cortex; Right medulla renis regional function value or left medulla renis regional function value≤-20 be decided to be-1; The aldosterone function value <-20 of interstitial, the adrenal medullary hormone function value <-20 of interstitial

The follicle stimulating hormone function value <-20 of female's interstitial

The testosterone function value <-20 of man's interstitial

Meet arbitrary Risk-warning standard and eagle in above clinical indices simultaneously and drill arbitrary Risk-warning standard in the directive function value of whole body health scanning system, then hypopituitarism risk is judged to be 1, there is hypopituitarism risk.

Hyperpituitarism Risk-warning standard:

1, clinical indices;

I, medical history and sign: gigantism or acromegaly occur together mental disorder: (1) mental symptom: 1. personality change 2. paranoid state 3. manic or depressive state 4. dull-witted state.(2) nervous symptoms: be mainly pituitary adenoma local compression symptom as headache, tinnitus constriction of visual field, blurred vision, papilloedema and atrophy part (3) other: have hyperhidrosis, hypersexuality in early days.Later stage hyposexuality etc.

II, iconography (X-ray, CT, magnetic resonance) localization examination

III, Intervention around

Twenty-four-hour urine 17-KS (17-KS), 17OHS (17-OHCS) and UFC are all higher than normal value.

IV, thyroid function

Serum T 3, T4 and Thyroid Intaking I ¹³¹rate is higher than normally.

V, gonad function

Male's serum testosterone, urine 17-KS; Women's serum estradiol increases with urine estrogen (estrone, estradiol, estriol) level.

VI, antepituitary function

(1) serum TSH, LH, FSH, ACTH and GH can higher than normal values.

(2) ACTH secretion test

(3) growth hormone (GH) secretion test

(4) lactotropin (PRL) secretion test

(5) promoting sexual gland hormone (Gn) secretion test

(6) thyrotropin (TSH) secretion test

2, eagle drills the directive function value of whole body health scanning system:

Hypophyseal area domain-functionalities value >-20, hypothalamic areas function value > 20, vassopressin function value > 20;

Adrenal gland: the regional function value of right adrenal gland cortex, the regional function value > 20 of left adrenal gland cortex; Right medulla renis regional function value, left medulla renis regional function value > 20; The aldosterone function value > 20 of interstitial, the adrenal medullary hormone function value > 20 of interstitial

The follicle stimulating hormone function value > 20 of female's interstitial

The testosterone function value > 20 of man's interstitial

Meet arbitrary Risk-warning standard and eagle in above clinical indices simultaneously and drill arbitrary Risk-warning standard in the directive function value of whole body health scanning system, then hyperpituitarism risk is judged to be 1, there is hyperpituitarism risk.

Model training module: using the training sample in sample set as input, using hormonal system health risk history early warning result as output, adopt extreme learning machine ELM and support vector machines model respectively, carry out the training of human endocrine system health risk warning model, training obtains human endocrine system health risk warning model;

Extreme learning machine develops from the neutral net of single hidden layer, and have and be easy to realize, and speed is fast, the features such as generalization ability is strong.Extreme learning machine has lacked output layer than single hidden layer Feedback Neural Network and has been biased, and inputs weight w _ib is biased with hidden layer _irandom generation, does not need adjustment, and the so whole network only remaining weight beta one that exports is not determined.

Make the output of neutral net equal sample label, represent such as formula (1)

T＝Hβ(1)

The solution obtaining formula (1) can complete the structure of whole neutral net.When the number L of hidden neuron and the number N of training sample is consistent, i.e. L=N, matrix H is Invertible Square Matrix, so gets output weight beta=H ^-1t, can make neutral net with 0 error fit mapping function f:x → y.

But, in most of the cases, the number L of hidden neuron is the number N much smaller than training sample, i.e. L < < N, at this moment there is not the solution that formula (1) is set up, therefore then ask the solution making loss function C minimum, represent such as formula (2).

$\widehat{\mathrm{\&beta;}}=\arg \min \left|\right|T-H\mathrm{\&beta;}|{|}_F---\left(2\right)$

According to Minimum-Norm Solution criterion (namely meeting min||H β-T|| and min|| β ||) simultaneously, then there is following Minimum Norm and select least square solution in formula (2):

$\widehat{\mathrm{\&beta;}}=H^{+}T---\left(3\right)$

Wherein H ⁺the Moore-Penrose augmentation being hidden layer response matrix H is inverse, is called for short pseudoinverse.H ⁺there is multiple account form.In the middle of extreme learning machine, Orthogonal Method is often used in H ⁺calculating: work as H ^twhen H is nonsingular, HH ⁺=(H ^th) ^-1h ^t; Work as HH ^ttime nonsingular, H ⁺=H ^t(HH ^t) ^-1.

Algorithm 1 summarizes the flow process of extreme learning machine.

Input: training sample set hidden neuron number L and excitation function g ()

Output: export weight beta

1. stochastic generation w _i, b _i, i=1 ..., L;

2. calculate H;

3. calculate β according to formula (3);

When when calculating complete, a single hidden layer Feedback Neural Network just completes.For the test sample book x of a label the unknown, can be inferred its label by single hidden layer Feedback Neural Network, its label can be inferred with following formula:

$f_L\left(x\right)=h\left(x\right)\widehat{\mathrm{\&beta;}}---\left(4\right)$

Wherein h (x)=[G (w ₁, b ₁, x) ... G (w _l, b _l, x)] and be the response of neutral net hidden layer about x.

Extreme learning machine has following feature compared with traditional single hidden layer Feedback Neural Network algorithm solved based on gradient:

(1) speed of extreme learning machine quickly, do not need study in other words, and (complexity is o (min (L only to need to export weight beta ³, N ³))) obtain; And the every iteration of back-propagation algorithm once needs to adjust n × (L+1)+L × (m+1) individual value, and back-propagation algorithm chooses less learning rate usually in order to ensure the stability of system, and learning time is lengthened greatly.Therefore extreme learning machine is very huge in this method advantage, and in an experiment, extreme learning machine often just completes computing within the several seconds.Even and the application very little when the single hidden layer neutral net of training one of some more classical algorithms also will spend a large amount of time, seeming these algorithms also exists a pseudo-velocity barrier that cannot go beyond.

(2) in most application, the generalization ability of extreme learning machine is greater than and is similar to this kind of algorithm based on gradient of error backpropagation algorithm.

(3) the traditional algorithm based on gradient needs in the face of such as Local Minimum, the problems such as suitable learning rate, over-fitting, and machine selects learning machine to settle direct construction at one go to play single hidden layer Feedback Neural Network, avoid these reluctant thorny problems.

Extreme learning machine, due to these advantages, is widely applied extreme learning machine carries out classification prediction during in a lot of field.

And support vector machines is by introducing kernel function, sample vector is mapped to high-dimensional feature space, in higher dimensional space, then constructs optimal classification surface, obtain linear optimal decision function.SVM can carry out the over-fitting of inhibition function by the gap metric controlling hyperplane; By adopting, kernel function is ingenious solves problem of dimension, avoids the directly related of learning algorithm computation complexity and sample dimension.

SVM defines optimum linearity hyperplane, and be converted into solve quadratic programming problem finding an optimum linearity hyperplane, and then based on Mercer theorem, pass through nonlinear mapping, sample space is mapped to high-dimensional feature space, thus uses the nonlinearity problem in linear method solution sample space.Support vector machine proposes for two category classifications.Suppose given training sample { x _i, y _i, i=1,2 ..., l, x ∈ R ^d, y _i∈-1,1}, there is Optimal Separating Hyperplane wx+b=0, for making classifying face all samples correctly classified and possess class interval, must meet

y _i[(w·x _i)+b]-1≥0(5)

Can calculate class interval is

$\underset{\left\{x_i\right|y_i=+1\}}{\min }\frac{w\&CenterDot;x_i+b}{\left|\right|w\left|\right|}-\underset{\left\{x_i\right|y_i=-1\}}{\min }\frac{w\&CenterDot;x_i+b}{\left|\right|w\left|\right|}=\frac{2}{\left|\right|w\left|\right|}---\left(6\right)$

Require maximum class interval 2/||w||, namely require to minimize || w||.Then solve optimal separating hyper plane problem and just can be expressed as constrained optimization problems, namely under the constraint of formula (5), minimization function

$\mathrm{\&Psi;}\left(w\right)=\frac{1}{2}\left|\right|w|{|}^2=\frac{1}{2}(w\&CenterDot;w)---\left(7\right)$

Introduce Lagrange function:

$L=\frac{1}{2}\left|\right|w|{|}^2-\underset{i=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i{y}_i(w\&CenterDot;x_i+b)+\underset{i=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i---\left(8\right)$

Wherein, α _i> 0 is Lagrange coefficient.Formula (8) asked local derviation to w and b respectively and makes it equal 0, just the problems referred to above can be converted into simple dual problem.

$\frac{\&part;L}{\&part;w}=w-\underset{i=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i{y}_i{x}_i=0---\left(9\right)$

$\frac{\&part;L}{\&part;b}=-\underset{i=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i{y}_i=0---\left(10\right)$

Formula (9) and formula (10) are brought in formula (8), antithesis optimization problem can be obtained: the maximum solving lower array function

$W\left(\mathrm{\&alpha;}\right)=\underset{i=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i-\frac{1}{2}\underset{i,j=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i{\mathrm{\&alpha;}}_j{y}_i{y}_j(x_i\&CenterDot;x_j)$

s.t.y _i[(w·x _i)+b]-1≥0(11)

$\underset{i=1}{\overset{n}{\&Sigma;}}{y}_i{\mathrm{\&alpha;}}_i=0$

α _i≥0,i＝1,…,l

This is the quadratic function extreme-value problem (QP, QuadraticProgramming) under an inequality constraints.According to Karush-Kuhn-Tucker (KKT) condition, the solution of this optimization problem must meet:

α _i{y _i[(w·x _i)+b]-1}＝0,i＝1,…,l(12)

Therefore, the α that most sample is corresponding _ifor 0, α _i≠ 0 is called support vector (SVs) corresponding to making the sample that in formula (5), equal sign is set up.In algorithm of support vector machine, support vector is the key element in training set, and they are nearest from decision boundary.If remove the training sample that other are all, then re-start training, identical classifying face will be obtained.

After solving above-mentioned quadratic programming problem, then categorised decision function can be expressed as

$f\left(x\right)=\mathrm{sgn}\left(\underset{i=1}{\overset{n}{\&Sigma;}}{\mathrm{\&alpha;}}_i^{*}{y}_i\right(x_i\&CenterDot;x)+b^{*})---\left(13\right)$

Summation in formula is only carried out support vector, namely only has non-vanishing α _icorresponding training sample determines classification results, and other sample and classification results have nothing to do.B ^*it is classification thresholds.When training sample set is linearly inseparable, introduce non-negative slack variable ξ _i, i=1,2 ..., l, the optimal problem of Optimal Separating Hyperplane is

$\underset{w,b,{\mathrm{\&xi;}}_i}{\min }\frac{1}{2}\left|\right|w|{|}^2+C\underset{i=1}{\overset{l}{\&Sigma;}}{\mathrm{\&xi;}}_i---\left(14\right)$

Its dual problem is maximum α being solved to lower array function:

$\underset{i=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i-\frac{1}{2}\underset{i,j=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i{\mathrm{\&alpha;}}_j{y}_i{y}_j(x_i\&CenterDot;x_j)$

s.t.y _i[(w·x _i)+b]≥1-ξ _i(15)

$\underset{i=1}{\overset{l}{\&Sigma;}}{y}_i{\mathrm{\&alpha;}}_i=0$

0≤α _i≤C,ξ _i≥0,i＝1,…,l

Wherein C > 0 is a constant, and be called error punishment parameter, it controls the degree of dividing sample to punish to mistake; ξ _iit is the non-negative slack variable introduced when training sample linearly inseparable.

When sample linearly inseparable, categorised decision function also can be expressed as the form of formula (13).

For Nonlinear Classification problem, then adopt suitable interior Product function K (x _i, x _j) just can realize the linear classification after a certain nonlinear transformation, the object function now optimized becomes

$Q\left(x\right)=\underset{i=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i-\frac{1}{2}\underset{i,j=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i{\mathrm{\&alpha;}}_j{y}_i{y}_{j}K(x_i\&CenterDot;x_j)---\left(16\right)$

And corresponding categorised decision function representation is

$f\left(x\right)=\mathrm{sgn}\left(\underset{i=1}{\overset{l}{\&Sigma;}}{\mathrm{\&alpha;}}_i^{*}{y}_{i}K\right(x_i\&CenterDot;x)+b^{*})---\left(17\right)$

Above categorised decision function is exactly support vector machine.Can see, former problem is converted into dual problem, make the complexity calculated no longer depend on space dimensionality, but depend on sample number, the support vector number especially in sample, this feature of support vector machine makes it can effectively tackle higher-dimension problem.

The feature of support vector machine

1. in system structure simple surfaces support vector machine similar with in three_layer planar waveguide, but they have basic difference.Support vector machine structure is very simple, does not need too much priori.Its hidden layer is determined automatically by algorithm, can with the needs of practical problem adaptive adjustment scale and size, there is not the structure choice problem of similar neutral net.And the hidden layer number of neutral net and the interstitial content of every layer determine all in advance, in the algorithm of neutral net, only automatically produce network weight.

2. Global Optimality support vector machine is by solving optimal hyperlane to learn, the Nonlinear Classification face in the corresponding raw mode space of the hyperplane in high-dimensional feature space.Finding the problem of optimal hyperlane is utilize Lagrange optimization method to be converted into quadratic programming problem, and what can ensure that algorithm of support vector machine obtains is globally optimal solution, makes it become a kind of outstanding learning algorithm.In neutral net, the possibility of result obtained is locally optimal solution.Particularly when the dimension of training sample is higher, may there is many local extremums in higher dimensional space, and have larger difference between different local extremums, and the training and testing result of neutral net can present larger randomness.

3. Generalization Ability strong support vector machine Corpus--based Method theory of learning, adopt structural risk minimization, can do reasonably compromise between empiric risk and model complexity, the decision rules obtained by limited training sample the popularization performance of learning machine can be improved, even if still can obtain less error to independently test set as far as possible.Neutral net have employed and keeps fiducial range and the strategy minimizing empiric risk, but clear and definite foundation instructs how structural learning machine makes fiducial range minimum; And SVM adopt maintenance empiric risk is fixed and minimizes the method for fiducial range.From the angle obtaining good Generalization Ability, SVM is obviously much brilliant than neutral net.

Model measurement module: utilize the test sample book in sample set, tests the training result of the human endocrine system health risk warning model that the human endocrine system health risk warning model adopting extreme learning machine ELM to train and employing support vector machines model training go out respectively;

Model selection module: select test result to evaluate height and the high human endocrine system health risk warning model of test result accuracy rate as final human endocrine system health risk warning model.

Human endocrine system health Risk-warning unit: utilize human endocrine system health risk warning model, the clinical indices data with the function value of organ, hormonal system are respectively organized based on the current human endocrine system collected, carry out human endocrine system health Risk-warning, obtain human endocrine system health Risk-warning result, export hormonal system health risk early warning result display to.

The sub-health population ratio of hormonal system and endocrine organ is in table 1.

Table 1 hormonal system, arteria coronaria, heart sub-health population ratio

Hormonal system	0.948
		Thyroid is hyperfunction	0.128
Thyroid goes down	0.574
		Hypophysis is hyperfunction	0.088
Hypophysis goes down	0.829

Training and the predictablity rate of hormonal system diagnosis are as shown in table 2.

Table 2ELM and SVM predictablity rate compares

Result shows, extreme learning machine ELM is higher than support vector machines in accuracy rate, and therefore present embodiment selection limit learning machine ELM model is as final human endocrine system health risk warning model.

In crowd, hormonal system subhealth state problem is ubiquitous, sub-health population and healthy population many eagles drill with clinical indices on there is certain diversity, based on large data sets, it is feasible that limit of utilization learning machine ELM model carries out this technology of subhealth state predicted application to the hormonal system of sub-health population, accuracy rate is high, more comprehensively and objectively can carry out early warning to the sub-health status carrying out hormonal system.

Human endocrine system health Risk-warning unit: utilize human endocrine system health risk warning model, the function value of tissue and organ, the clinical indices data of hormonal system are respectively digested based on the current human body collected, carry out human endocrine system health Risk-warning, obtain human endocrine system health Risk-warning result, export hormonal system health risk early warning result display to.

Hormonal system health risk early warning result display: the hormonal system health risk early warning result that display hormonal system health risk warning data processor exports;

The outfan of health scanning system connects the input of hormonal system health risk warning data processor, the outfan hormonal system health risk early warning result display of hormonal system health risk warning data processor.

As shown in Figure 3, human endocrine system health Warning System is utilized to carry out the process of human endocrine system health Risk-warning as follows:

Step 1: utilize health scanning system to scan and assess human body hormonal system and respectively organize the function value with organ;

Step 2: the clinical indices data obtaining hormonal system, utilize human endocrine system health risk warning model, respectively organize based on human endocrine system and carry out human endocrine system health Risk-warning with the clinical indices data of the function value of organ and hormonal system;

The human endocrine system that is input as of described human endocrine system health risk warning model respectively organizes the clinical indices data with the function value of organ and hormonal system, exports as hormonal system health risk early warning result;

Step 2.1: the human endocrine system gathering the scanning of health scanning system and assessment respectively organizes the clinical indices data with organ, hormonal system;

Step 2.2: the human endocrine system according to scanning and assessment is respectively organized and the function value historical data of organ and the clinical indices historical data of hormonal system and corresponding hormonal system health risk history early warning result training human body hormonal system health risk Early-warning Model;

Step 2.2.1: respectively organize according to the human endocrine system of scanning and assessment and generate sample set with the function value historical data of organ and the clinical indices historical data of hormonal system and corresponding hormonal system health risk history early warning result, using a part of sample in sample set as training sample, all the other are as test sample book;

Step 2.2.2: using the training sample in sample set as input, using hormonal system health risk history early warning result as output, adopt extreme learning machine ELM and support vector machines model respectively, carry out the training of human endocrine system health risk warning model, training obtains human endocrine system health risk warning model;

Step 2.2.3: utilize the test sample book in sample set, tests the training result of the human endocrine system health risk warning model that the human endocrine system health risk warning model adopting extreme learning machine ELM to train and employing support vector machines model training go out respectively;

Step 2.2.4: select test result to evaluate height and the high human endocrine system health risk warning model of training result accuracy rate as final human endocrine system health risk warning model.

Step 2.3: utilize human endocrine system health risk warning model, the clinical indices data with the function value of organ, hormonal system are respectively organized based on the current human endocrine system collected, carry out human endocrine system health Risk-warning, obtain human endocrine system health Risk-warning result;

Step 3: hormonal system health risk early warning result display display hormonal system health risk early warning result.

Claims (3)

1. a human endocrine system health Warning System, is characterized in that, comprising:

Health scanning system: respectively organize the function value with organ for scanning and assess human body hormonal system, comprise the function value of hypophysis region, hypothalamic areas, vassopressin, aldosterone, testosterone;

Hormonal system health risk warning data processor: the clinical indices data obtaining hormonal system, utilize human endocrine system health risk warning model, respectively organize based on human endocrine system and carry out human endocrine system health Risk-warning with the clinical indices data of the function value of organ and hormonal system; The human endocrine system that is input as of described human endocrine system health risk warning model respectively organizes the clinical indices data with the function value of organ and hormonal system, exports as hormonal system health risk early warning result; The clinical indices data of described hormonal system comprise: hormonal system medical history, body are looked into, lab index; Described hormonal system health risk early warning result comprises: hormonal system health status, thyroid are hyperfunction, thyroid goes down, hypophysis is hyperfunction, hypophysis goes down;

Hormonal system health risk early warning result display: the hormonal system health risk early warning result that display hormonal system health risk warning data processor exports;

The outfan of health scanning system connects the input of hormonal system health risk warning data processor, and the outfan of hormonal system health risk warning data processor connects hormonal system health risk early warning result display.

2. human endocrine system health Warning System according to claim 1, is characterized in that, described hormonal system health risk warning data processor comprises:

Data acquisition unit: the human endocrine system gathering the scanning of health scanning system and assessment respectively organizes the clinical indices data with the function value of organ, hormonal system;

Human endocrine system health risk warning model sets up unit: the human endocrine system according to scanning and assessment is respectively organized and the function value historical data of organ and the clinical indices historical data of hormonal system and corresponding hormonal system health risk history early warning result training human body hormonal system health risk Early-warning Model;

Human endocrine system health Risk-warning unit: utilize human endocrine system health risk warning model, the clinical indices data with the function value of organ, hormonal system are respectively organized based on the current human endocrine system collected, carry out human endocrine system health Risk-warning, obtain human endocrine system health Risk-warning result, export hormonal system health risk early warning result display to.

3. human endocrine system health Warning System according to claim 2, is characterized in that, described human endocrine system health risk warning model is set up unit and comprised:

Sample generation module: respectively organize according to the human endocrine system of scanning and assessment and generate sample set with the function value historical data of organ and the clinical indices historical data of hormonal system and corresponding hormonal system health risk history early warning result, using a part of sample in sample set as training sample, all the other are as test sample book;

Model training module: using the training sample in sample set as input, using hormonal system health risk history early warning result as output, adopt extreme learning machine ELM and support vector machines model respectively, carry out the training of human endocrine system health risk warning model, training obtains human endocrine system health risk warning model;

Model measurement module: utilize the test sample book in sample set, tests the training result of the human endocrine system health risk warning model that the human endocrine system health risk warning model adopting extreme learning machine ELM to train and employing support vector machines model training go out respectively;

Model selection module: select test result to evaluate height and the high human endocrine system health risk warning model of test result accuracy rate as final human endocrine system health risk warning model.