CN106407697A - Chronic fatigue syndrome prediction method and prediction system based on incremental neural network model - Google Patents
Chronic fatigue syndrome prediction method and prediction system based on incremental neural network model Download PDFInfo
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Abstract
The invention discloses a chronic fatigue syndrome prediction method based on an incremental neural network model. The chronic fatigue syndrome prediction method comprises following steps that a database of chronic fatigue syndrome daily data is established; a neural network model is trained; daily life data is acquired and sent to a server; intraday data is extracted from a user daily data recording chart to form an n-dimensional vector, after normalization processing, the n-dimensional vector is input into a chronic fatigue syndrome pathology neural network model to carry out chronic fatigue syndrome dangerous level probability prediction; whether the chronic fatigue syndrome dangerous level value W is larger than or equal to 3 or not is determined by an intelligent household chronic fatigue syndrome nursing device; when the user receives caution from a warning indicator, the user goes to the hospital for check-up himself, and sends the check-up result back to the server, and the server determines whether the check-up result is correct or not; when the check-up result is wrong, an incremental algorithm is executed, and the neural network model is dynamically corrected. The chronic fatigue syndrome prediction method based on the incremental neural network model is accurate in prediction, and the neural network model is customized for each user.
Description
Technical field
The invention belongs to field of medical technology, more particularly to a kind of confirmed fatigue based on increment type neural network model
Syndrome Forecasting Methodology and prognoses system.
Background technology
Currently domestic each health management system arranged be respectively provided with chronic fatigue syndrome prediction and evaluation, its use prediction mode be
Data Matching.Its principle is that by system matches fixed data and then personal lifestyle data entry system is shown ill probability.But
Due to complexity, the unpredictability of human body and disease, in the form of expression with information for the bio signal, Changing Pattern (itself
Change after change and medical intervention) on, it is detected and signal representation, the data of acquisition and the analysis of information, decision-making etc.
All there is extremely complex non-linear relationship in all many-sides.So being sieved using the data that traditional Data Matching can only be blindness
Look into it is impossible to the logic association that judges between data and data and variable, the codomain deviation obtaining is big, causes the spy of system prediction
The opposite sex is very poor, thus current domestic health management system arranged cannot effectively personal chronic fatigue syndrome be carried out accurately pre-
Survey.
Most of before this is all using BP neural network model to chronic fatigue syndrome prediction, but when new detection number
According to when generation, it is necessary to train neural network model again, operation efficiency is extremely low.And when system user scale increases
Afterwards, server will be unable to complete in time training mission.
Content of the invention
The purpose of the present invention is that and overcomes the deficiencies in the prior art, there is provided one kind is based on increment type neural network model
Chronic fatigue syndrome Forecasting Methodology and prognoses system, the present invention by neural network model train predict a large amount of patient in hospital
Pathological data, finds chronic fatigue syndrome pathology and chronic fatigue syndrome earlier life variations in detail, clinical symptoms, detection
Standard value, high-risk group's feature, the logic association between this several causes of disease and variable, ultimately form and chronic fatigue syndrome are suffered from
The chronic fatigue syndrome pathology neural network model of sick probability Accurate Prediction, the present invention passes through to gather user's daily life number
According to the periodicity of its data of active analysis, regularity are used eventually through chronic fatigue syndrome pathology Neural Network model predictive
Family suffer from chronic fatigue syndrome probability, remind user's instant hospitalizing in the way of visual effect, work as Neural Network model predictive
When inaccurate, neural network model is constantly revised by increasable algorithm, be somebody's turn to do with setting up for each equipment user to train to be directed to
The neural network model of user, with the increase of use time, to set up the neural network model that this user is made to measure,
Accuracy rate is greatly improved.
To achieve these goals, the invention provides a kind of confirmed fatigue based on increment type neural network model is comprehensive
Disease Forecasting Methodology, comprises the steps：
Step (1), acquisition hospital's chronic fatigue syndrome etiology and pathology data source and the daily monitoring data of patient, thus build
The daily data database of vertical chronic fatigue syndrome；
Step (2), the daily data database of chronic fatigue syndrome set up according to step (1) are off-line manner to god
It is trained through network model, to obtain the chronic fatigue syndrome pathology neural network model training；
Step (3), by intelligent monitoring device, the daily life data of user is acquired, and will collection daily life
Live data sends to server, and server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, extract same day data, form n-dimensional vector, and n-dimensional vector is done
Carry out confirmed fatigue in the chronic fatigue syndrome pathology neural network model training in input step (2) after normalized
Syndrome degree of danger probabilistic forecasting, obtains chronic fatigue syndrome probability results array P, and server will be general for highest in array P
Rate corresponding chronic fatigue syndrome degree of danger value W sends wired home chronic fatigue syndrome care appliances to；
Step (5), the chronic fatigue syndrome of wired home chronic fatigue syndrome care appliances the reception server transmission
After degree of danger value W, judge whether chronic fatigue syndrome degree of danger value W is more than or equal to 3, if greater than equal to 3, then warn
Device warns to remind user, and if less than 3, then attention device does not warn；
Step (6), when user receive attention device warning when, user voluntarily removes examination in hospital, and inspection result is passed through
Wired home chronic fatigue syndrome care appliances send back server, and server judges whether inspection result is correct, if inspection
Come to an end fruit mistake, then explanation chronic fatigue syndrome pathology Neural Network model predictive is inaccurate, if inspection result is correct,
Illustrate that chronic fatigue syndrome pathology Neural Network model predictive is accurate；
Step (7), when inspection result mistake, from the daily data logger of user extract m days in record preserve to
In incremental data table, when the record quantity in incremental data table is more than h bar, execute increasable algorithm, comprehensive to confirmed fatigue
Disease pathology neural network model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
Further, the input layer of neural network model is n node, and hidden layer number is n*2+1, and output layer is 1
Node, extracts k bar record from chronic fatigue syndrome daily data database table and is trained, and every record is a n dimension
Vector, all data before use first through normalized so as to numerical value is interval in [0,1], then execution following steps are to god
It is trained through network model：
1) one n-dimensional vector of input, to neural network model, calculates all of weight vector in neural network model defeated to this
Enter the distance of n-dimensional vector, closest neuron is as won neuron, its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck
Domain；α (t) is learning rate, and it is as the function that the increase of iterationses is gradually successively decreased, and span is [0 1], through multiple
It is 0.71 that Optimal learning efficiency is chosen in experiment；DjIt is the distance of neuron j and triumph neuron；σ (t) is as the letter that the time successively decreases
Number；Iteration all input n-dimensional vectors is input in neural network model and is trained each time, when the iteration reaching regulation
After number of times, neural network model training terminates.
Further, inspection result is sent back the result letter of server by wired home chronic fatigue syndrome care appliances
Breath form be：{ the chronic fatigue syndrome degree of danger value of the actual judgement of doctor }, server after receiving object information,
Judge whether inspection result is correct.
Further, the increasable algorithm carrying out dynamic corrections to chronic fatigue syndrome pathology neural network model is：
Vectorial for every in incremental data table V { V1,V2,…,Vn, it is sent in neural network model learning function
Row study, learning procedure is as follows：
1) first to output layer, each weight vector is assigned little random number and is done normalized, then utilizes input mode vector V
Meansigma methodss Avg (V), be initialized as the weights of unique neuron in the 0th layer of neural network model, and be set to win nerve
Unit, calculates its quantization error QE；
2) expand out 2 × 2 structures SOM from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, initialize the power of this 4 neurons
Value；The input vector set Ci of i-th neuron is set to sky, main label is set to NULL, the main label ratio r of neuron ii
It is set to 0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) select a vectorial VX from VXiDo following judgement：
If VXiFor the data of not tape label, then calculate its Euclidean distance with each neuron, chosen distance is the shortest
Neuron is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe maximum neuron of value is made
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiLabel identical neuron, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vectorial set W=W ∪ that wins
{VXi, calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training,
Go to step 4)；
6) quantization error QE of each neuron in this neural network model after calculating is adjustedi, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiThe set constituting for all input vectors being mapped to neuron i；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall label data on neuron
Sum, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in this SOM, turn step
Rapid 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will
The subnet newly growing increases in the subnet queue of Layer+1 layer；
If being not inserted into new neuron in SOM also do not grow new subnet, illustrate that the training of this subnet completes；
7) for all 2 × 2 structures SOM of the Layer+1 layer newly expanded out, iteration operating procedure 3)～5) to it again
It is trained, until neural network model no longer produces new neuron and new layering, whole training terminates.
Further, in step (4), chronic fatigue syndrome probability results array P obtaining is one 6 dimension variable,
Its 6 dimension variable is respectively 6 chronic fatigue syndrome degree of danger probability, 6 chronic fatigue syndrome degree of danger probability
Corresponding chronic fatigue syndrome degree of danger value W of middle maximum probability sends the nursing of wired home chronic fatigue syndrome to and sets
Standby；In step (5), represent preferable when chronic fatigue syndrome degree of danger value W=1, represent normal during W=2, during W=3
Represent subhealth state, represent dangerous during W=4, represent abnormally dangerous during W=5, during W=6, represent ill.
Further, if user includes health check-up by other means and checks oneself, learn and oneself have suffered from confirmed fatigue comprehensively
Disease, and the attention device of wired home chronic fatigue syndrome care appliances does not warn then it represents that wired home confirmed fatigue is comprehensive
Close disease care appliances and judge inaccurate, now execution step (6)～(7), wired home chronic fatigue syndrome care appliances handle
Object information is sent on server.
Present invention also offers a kind of prognoses system of described chronic fatigue syndrome Forecasting Methodology, set including intelligent monitoring
Standby, smart machine data acquisition unit, server and wired home chronic fatigue syndrome care appliances, described intelligent monitoring device
It is connected with described smart machine data acquisition unit, described smart machine data acquisition unit passes through communication device one and described service
Device network communication, described wired home chronic fatigue syndrome care appliances are passed through communication device two and are led to described server network
News.
Further, described wired home chronic fatigue syndrome care appliances are provided with attention device.
Further, described intelligent monitoring device includes Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent horse
Bucket and Intelligent light sensing equipment.
Beneficial effects of the present invention：
1st, the present invention is trained by neural network model and predicts a large amount of patient in hospital pathological data, finds confirmed fatigue comprehensive
Disease pathology and chronic fatigue syndrome earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, this is several
Logic association between the item cause of disease and variable, ultimately form the confirmed fatigue to chronic fatigue syndrome illness probability Accurate Prediction
Syndrome pathology neural network model, the present invention pass through gather user's daily life data, the periodicity of its data of active analysis,
Regular suffer from chronic fatigue syndrome danger journey eventually through chronic fatigue syndrome pathology Neural Network model predictive user
Degree probability, reminds user's instant hospitalizing and prevention in the way of visual effect.
2nd, when Neural Network model predictive is inaccurate, neural network model is constantly revised, to be directed to by increasable algorithm
Each equipment user sets up the neural network model training for this user, with the increase of use time, to set up to this
The neural network model that user makes to measure, accuracy rate is greatly improved.
Brief description
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
Have technology description in required use accompanying drawing be briefly described it should be apparent that, drawings in the following description be only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, acceptable
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the flow chart of the embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings invention is further illustrated, but be not limited to the scope of the present invention.
Embodiment
As shown in figure 1, a kind of chronic fatigue syndrome prediction based on increment type neural network model that the present invention provides
Method, comprises the steps：
Step (1), acquisition hospital's chronic fatigue syndrome etiology and pathology data source and the daily monitoring data of patient, thus build
The daily data database of vertical chronic fatigue syndrome；
Wherein daily monitoring data is 21 item data, and its 21 item data is the age, sex, heart rate, body fat, and systolic pressure is relaxed
Open and press, smoking capacity (daily), the amount of drinking (daily), the amount of drinking water and frequency, body weight, total urination time, urine color, times of defecation,
Whether constipation, BMI index, body temperature, pursue an occupation, the length of one's sleep and quality, 21 item data such as travel distance (daily), the present invention
Set up 21 dimensional vectors with 21 item data；
Step (2), the daily data database of chronic fatigue syndrome set up according to step (1) are off-line manner to god
It is trained through network model, to obtain the chronic fatigue syndrome pathology neural network model training；
Step (3), by intelligent monitoring device, the daily life data of user is acquired, and will collection daily life
Live data sends to server, and server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, extract same day data, form n-dimensional vector, and n-dimensional vector is done
Carry out confirmed fatigue in the chronic fatigue syndrome pathology neural network model training in input step (2) after normalized
Syndrome degree of danger probabilistic forecasting, obtains chronic fatigue syndrome probability results array P, and server will be general for highest in array P
Rate corresponding chronic fatigue syndrome degree of danger value W sends wired home chronic fatigue syndrome care appliances to；
Wherein in step (4), chronic fatigue syndrome probability results array P obtaining is one 6 dimension variable, its 6 dimension
Variable is respectively 6 chronic fatigue syndrome degree of danger probability, highest in 6 chronic fatigue syndrome degree of danger probability
Probability corresponding chronic fatigue syndrome degree of danger value W sends wired home chronic fatigue syndrome care appliances to.
Step (5), the chronic fatigue syndrome of wired home chronic fatigue syndrome care appliances the reception server transmission
After degree of danger value W, judge whether chronic fatigue syndrome degree of danger value W is more than or equal to 3, if greater than equal to 3, then warn
Device warns to remind user, and if less than 3, then attention device does not warn；
Wherein in step (5), represent preferable when chronic fatigue syndrome degree of danger value W=1, just represent during W=2
Often, represent subhealth state during W=3, represent dangerous during W=4, represent abnormally dangerous during W=5, during W=6, represent ill.The i.e. present invention
Chronic fatigue syndrome degree of danger is divided into 6 grades, more accurate and visual.
Step (6), when user receive attention device warning when, user voluntarily removes examination in hospital, and inspection result is passed through
Wired home chronic fatigue syndrome care appliances send back server, and server judges whether inspection result is correct, if inspection
Come to an end fruit mistake, then explanation chronic fatigue syndrome pathology Neural Network model predictive is inaccurate, if inspection result is correct,
Illustrate that chronic fatigue syndrome pathology Neural Network model predictive is accurate；
Wherein inspection result is sent back the object information of server by wired home chronic fatigue syndrome care appliances
Form is：{ the chronic fatigue syndrome degree of danger value of the actual judgement of doctor }, server, after receiving object information, judges
Whether inspection result is correct.
Step (7), when inspection result mistake, from the daily data logger of user extract 7 days in record preserve to
In incremental data table, when the record quantity in incremental data table is more than 100, execute increasable algorithm, comprehensive to confirmed fatigue
Close disease pathology neural network model and carry out dynamic corrections；
Step (8), repeat step (3)～(7).
The input layer of the neural network model of the present invention is 21 nodes, and hidden layer number is 43, and output layer is 6 nodes
(i.e. 6 chronic fatigue syndrome degree of danger probability), extracts from chronic fatigue syndrome daily data database table
Article 400000, record is trained, and every record is 21 dimensional vectors, and all data first through normalized, make before use
Its numerical value is interval in [0,1], and then execution following steps are trained to neural network model：
1) one 21 dimensional vector of input, to neural network model, calculate all of weight vector in neural network model defeated to this
Enter the distance of 21 dimensional vectors, closest neuron is as won neuron, its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck
Domain；α (t) is learning rate, and it is as the function that the increase of iterationses is gradually successively decreased, and span is [0 1], through multiple
It is 0.71 that Optimal learning efficiency is chosen in experiment；DjIt is the distance of neuron j and triumph neuron；σ (t) is as the letter that the time successively decreases
Number；Iteration all input n-dimensional vectors is input in neural network model and is trained each time, when the iteration reaching regulation
After number of times, neural network model training terminates.
The increasable algorithm carrying out dynamic corrections to chronic fatigue syndrome pathology neural network model of the present invention is：
Vectorial for every in incremental data table V { V1,V2,…,Vn, it is sent in neural network model learning function
Row study, learning procedure is as follows：
1) first to output layer, each weight vector is assigned little random number and is done normalized, then utilizes input mode vector V
Meansigma methodss Avg (V), be initialized as the weights of unique neuron in the 0th layer of neural network model, and be set to win nerve
Unit, calculates its quantization error QE；
2) expand out 2 × 2 structures SOM from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, initialize the power of this 4 neurons
Value；The input vector set Ci of i-th neuron is set to sky, main label is set to NULL, the main label ratio r of neuron ii
It is set to 0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) select a vectorial VX from VXiDo following judgement：
If VXiFor the data of not tape label, then calculate its Euclidean distance with each neuron, chosen distance is the shortest
Neuron is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe maximum neuron of value is made
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiLabel identical neuron, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vectorial set W=W ∪ that wins
{VXi, calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training,
Go to step 4)；
6) quantization error QE of each neuron in this neural network model after calculating is adjustedi, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiThe set constituting for all input vectors being mapped to neuron i；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall label data on neuron
Sum, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in this SOM, turn step
Rapid 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will
The subnet newly growing increases in the subnet queue of Layer+1 layer；
If being not inserted into new neuron in SOM also do not grow new subnet, illustrate that the training of this subnet completes；
7) for all 2 × 2 structures SOM of the Layer+1 layer newly expanded out, iteration operating procedure 3)～5) to it again
It is trained, until neural network model no longer produces new neuron and new layering, whole training terminates.
If user includes health check-up by other means and checks oneself, learn that oneself has suffered from chronic fatigue syndrome, and intelligence
The attention device of family's chronic fatigue syndrome care appliances does not warn then it represents that the nursing of wired home chronic fatigue syndrome sets
For judging inaccurate, now execution step (6)～(7), wired home chronic fatigue syndrome care appliances pass object information
Deliver on server.
Present invention also offers a kind of prognoses system of described chronic fatigue syndrome Forecasting Methodology, set including intelligent monitoring
Standby, smart machine data acquisition unit, server and wired home chronic fatigue syndrome care appliances, described intelligent monitoring device
It is connected with described smart machine data acquisition unit, described smart machine data acquisition unit passes through communication device one and described service
Device network communication, described wired home chronic fatigue syndrome care appliances are passed through communication device two and are led to described server network
News.
It is provided with attention device on the described wired home chronic fatigue syndrome care appliances of the present invention.
The described intelligent monitoring device of the present invention includes Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool
With Intelligent light sensing equipment etc..
The present invention is trained by neural network model and predicts a large amount of patient in hospital pathological data, finds chronic fatigue syndrome
Pathology and chronic fatigue syndrome earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, this several
Logic association between the cause of disease and variable, ultimately form comprehensive to the confirmed fatigue of chronic fatigue syndrome illness probability Accurate Prediction
Close disease pathology neural network model, the present invention passes through to gather user's daily life data, the periodicity of its data of active analysis, rule
Rule property suffers from chronic fatigue syndrome probability, to regard eventually through chronic fatigue syndrome pathology Neural Network model predictive user
Feel that the mode of effect reminds user's instant hospitalizing and prevention.
All data of the present invention preserve to server, can significantly save calculating cost, and hardware configuration is low, thus
Price is also low.
The present invention carries communication device one and communication device two, by wifi from the Internet that is dynamically connected, and can protect for a long time
Hold online.Various intelligent monitoring devices can easily access present device by modes such as network or bluetooths, sets in acquisition
The daily life data of the monitoring of intelligent monitoring device, the data that therefore present device obtains can automatically be uploaded after standby mandate
It is real-time, accurate, polynary.
Because everyone physical trait is different, the data characteristicses being shown during chronic fatigue syndrome morbidity also can
Different.Therefore conventional is not high by the method accuracy rate of neural network prediction chronic fatigue syndrome.The present invention is directed to each
Equipment user sets up the neural network model training for this user, is running after a period of time, will produce to this user
Make neural network prediction model to measure, accuracy rate is greatly improved.
When neural network model is judged by accident, error message be will be feedbacked to by wired home chronic fatigue syndrome care appliances
Server, for this user's dynamic corrections neural network model, when similar characteristics data in this user next, will not be again
Erroneous judgement.Therefore, with the increase of use time, the judgement of the wired home chronic fatigue syndrome care appliances of the present invention will
More and more accurate.
Ultimate principle, principal character and the advantages of the present invention of the present invention have been shown and described above.The technology of the industry
, it should be appreciated that the present invention is not restricted to the described embodiments, the simply explanation described in above-described embodiment and description is originally for personnel
Invention principle, without departing from the spirit and scope of the present invention the present invention also have various changes and modifications, these change
Change and improvement both falls within scope of the claimed invention.Claimed scope by appending claims and its
Equivalent defines.
Claims (9)
1. a kind of chronic fatigue syndrome Forecasting Methodology based on increment type neural network model is it is characterised in that include as follows
Step：
Step (1), obtain hospital chronic fatigue syndrome and cure the disease etiology and pathology data source and the daily monitoring data of patient, thus building
The daily data database of vertical chronic fatigue syndrome；
Step (2), the daily data database of chronic fatigue syndrome set up according to step (1) are off-line manner to nerve net
Network model is trained, to obtain the chronic fatigue syndrome pathology neural network model training；
Step (3), by intelligent monitoring device, the daily life data of user is acquired, and will collection daily life number
According to sending to server, server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, extract same day data, form n-dimensional vector, and normalizing is done to n-dimensional vector
Carry out confirmed fatigue comprehensive in the chronic fatigue syndrome pathology neural network model training in input step (2) after change process
Disease degree of danger probabilistic forecasting, obtains chronic fatigue syndrome probability results array P, and server is by maximum probability pair in array P
Chronic fatigue syndrome degree of danger value W answered sends wired home chronic fatigue syndrome care appliances to；
Step (5), the chronic fatigue syndrome danger of wired home chronic fatigue syndrome care appliances the reception server transmission
After degree value W, judge whether chronic fatigue syndrome degree of danger value W is more than or equal to 3, if greater than equal to 3, then attention device is warned
Show to remind user, if less than 3, then attention device does not warn；
Step (6), when user receive attention device warning when, user voluntarily removes examination in hospital, and by inspection result pass through intelligence
Family's chronic fatigue syndrome care appliances send back server, and server judges whether inspection result is correct, if checking knot
Fruit mistake, then explanation chronic fatigue syndrome pathology Neural Network model predictive is inaccurate, if inspection result is correct, illustrates
Chronic fatigue syndrome pathology Neural Network model predictive is accurate；
Step (7), when inspection result mistake, from the daily data logger of user extract m days in record preserve to increment
In tables of data, when the record quantity in incremental data table is more than h bar, execute increasable algorithm, to chronic fatigue syndrome disease
Reason neural network model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
2. a kind of chronic fatigue syndrome Forecasting Methodology based on increment type neural network model according to claim 1,
It is characterized in that, the input layer of neural network model is n node, and hidden layer number is n*2+1, and output layer is 1 node, from
Extract k bar record in chronic fatigue syndrome daily data database table to be trained, every record is a n-dimensional vector, institute
There is data first before use through normalized so as to numerical value is in [0,1] interval, then execution following steps are to neutral net mould
Type is trained：
1) one n-dimensional vector of input, to neural network model, calculates all of weight vector in neural network model and ties up to this input n
The distance of vector, closest neuron is as won neuron, and its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron field；α
T () is through first field；α (t) is learning rate, and it is as the function that the increase of iterationses is gradually successively decreased, and span is [0
1], choosing Optimal learning efficiency through many experiments is 0.71；DjIt is the distance of neuron j and triumph neuron；σ (t) is as
The function that time successively decreases；All input n-dimensional vectors are all input in neural network model and are trained by iteration each time, when reaching
To after the iterationses of regulation, neural network model training terminates.
3. a kind of chronic fatigue syndrome Forecasting Methodology based on increment type neural network model according to claim 1,
It is characterized in that, inspection result is sent back the lattice of the object information of server by wired home chronic fatigue syndrome care appliances
Formula is：{ the chronic fatigue syndrome degree of danger value of the actual judgement of doctor }, server, after receiving object information, judges inspection
Whether the fruit that comes to an end is correct.
4. a kind of chronic fatigue syndrome Forecasting Methodology based on increment type neural network model according to claim 1,
It is characterized in that, the increasable algorithm carrying out dynamic corrections to chronic fatigue syndrome pathology neural network model is：
Vectorial for every in incremental data table V { V1,V2,…,Vn, it is sent in neural network model learning function and learned
Practise, learning procedure is as follows：
1) first to output layer, each weight vector is assigned little random number and is done normalized, then utilizes the flat of input mode vector V
Average Avg (V), is initialized as the weights of unique neuron in the 0th layer of neural network model, and is set to triumph neuron, meter
Calculate its quantization error QE；
2) expand out 2 × 2 structures SOM from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, the weights of this 4 neurons are initialized；Will
The input vector set Ci of i-th neuron is set to sky, and main label is set to NULL, the main label ratio r of neuron iiIt is set to
0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) select a vectorial VX from VXiDo following judgement：
If VXiFor the data of not tape label, then calculate its Euclidean distance with each neuron, chosen distance nerve the shortest
Unit is as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe maximum neuron of value is as obtaining
Victory neuron, updates this triumph neuron main label；
If can not find main label and VXiLabel identical neuron, then find and VXiClosest neuron i is as obtaining
Victory neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vectorial set W=W ∪ { VX that winsi,
Calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training, go to step
4)；
6) quantization error QE of each neuron in this neural network model after calculating is adjustedi, neuronal messages entropy EiAnd son
The average quantization error MQE of net, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiThe set constituting for all input vectors being mapped to neuron i；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall the total of label data on neuron
Number, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in this SOM, go to step 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will be newly long
The subnet going out increases in the subnet queue of Layer+1 layer；
If being not inserted into new neuron in SOM also do not grow new subnet, illustrate that the training of this subnet completes；
7) for all 2 × 2 structures SOM of the Layer+1 layer newly expanded out, iteration operating procedure 3)～5) it is re-started
Training, until neural network model no longer produces new neuron and new layering, whole training terminates.
5. a kind of chronic fatigue syndrome Forecasting Methodology based on increment type neural network model according to claim 1,
It is characterized in that, in step (4), chronic fatigue syndrome probability results array P obtaining is one 6 dimension variable, and its 6 dimension becomes
Amount is respectively 6 chronic fatigue syndrome degree of danger probability, general for highest in 6 chronic fatigue syndrome degree of danger probability
Rate corresponding chronic fatigue syndrome degree of danger value W sends wired home chronic fatigue syndrome care appliances to；In step
(5) in, represent preferable when chronic fatigue syndrome degree of danger value W=1, represent normal during W=2, during W=3, represent sub- strong
Health, represents dangerous, represents abnormally dangerous during W=5 during W=4, represent ill during W=6.
6. a kind of chronic fatigue syndrome Forecasting Methodology based on increment type neural network model according to claim 1,
It is characterized in that, if user includes health check-up by other means and checks oneself, learn that oneself has suffered from chronic fatigue syndrome, and intelligence
The attention device of energy family chronic fatigue syndrome care appliances does not warn then it represents that wired home chronic fatigue syndrome is nursed
Equipment judges inaccurate, now execution step (6)～(7), and wired home chronic fatigue syndrome care appliances are object information
It is sent on server.
7. a kind of prognoses system of chronic fatigue syndrome Forecasting Methodology described in employing claim 1～7 is it is characterised in that wrap
Include intelligent monitoring device, smart machine data acquisition unit, server and wired home chronic fatigue syndrome care appliances, described
Intelligent monitoring device is connected with described smart machine data acquisition unit, and described smart machine data acquisition unit passes through communication device
One with the communication of described server network, described wired home chronic fatigue syndrome care appliances pass through communication device two with described
Server network communicates.
8. according to claim 8 the prognoses system of chronic fatigue syndrome Forecasting Methodology it is characterised in that described intelligent family
It is provided with attention device on the chronic fatigue syndrome care appliances of front yard.
9. according to claim 8 the prognoses system of chronic fatigue syndrome Forecasting Methodology it is characterised in that described intelligence
Monitoring device includes Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool and Intelligent light sensing equipment.
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