CN106446560A - Hyperlipidemia prediction method and prediction system based on incremental neural network model - Google Patents
Hyperlipidemia prediction method and prediction system based on incremental neural network model Download PDFInfo
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Abstract
The invention discloses a hyperlipidemia prediction method based on an incremental neural network model. The hyperlipidemia prediction method comprises the following steps: establishing a database of daily data of hyperlipidemia; training a neural network model; acquiring daily living data and transmitting to a server; extracting data of a day from a daily data record table of a user, forming an n-dimensional vector, performing normalization processing, inputting into a hyperlipidemia pathology neural network model, and performing hyperlipidemia criticality probability prediction; determining whether a hyperlipidemia criticality value W is greater than or equal to 3 or not by using intelligent domestic hyperlipidemia nursing equipment; when the user receives an alert of an alarm, reminding the user to take inspection in a hospital, transmitting an inspection result to the server, and determining whether the inspection result is correct or not by the server; when the inspection result is wrong, implementing an incremental algorithm, and performing dynamic modification on the neural network model. The hyperlipidemia prediction method is accurate in prediction, and the neural network model can be customized for each user.
Description
Technical field
The invention belongs to field of medical technology, more particularly to a kind of hyperlipemia based on increment type neural network model
Forecasting Methodology and forecasting system.
Background technology
Currently domestic each health management system arranged be respectively provided with hyperlipemia prediction and evaluation, its use prediction mode be data
Join.Its principle is that by system matches fixed data and then personal lifestyle data entry system is shown ill probability.But due to people
The complexity of body and disease, unpredictability, in the form of expression of bio signal and information, Changing Pattern (Self-variation with
Change after medical intervention) on, it is detected and signal representation, the data of acquisition and the analysis of information, decision-making etc. are all multi-party
All there is extremely complex non-linear relationship in face.So using traditional Data Matching can only be blindness data examination it is impossible to
Judge the logic association between data and data and variable, the codomain deviation obtaining is big, causes the specificity ten of system prediction
Point poor, domestic health management system arranged effectively Accurate Prediction cannot be carried out to personal hyperlipemia so current.
Most of before this is all using BP neural network model to hyperlipemia prediction, but when new detection data produces
When it is necessary to train neural network model again, operation efficiency is extremely low.And after system user scale increases, service
Device 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
Hyperlipemia Forecasting Methodology and forecasting system, the present invention by neural network model train predict a large amount of patient in hospital pathology numbers
According to finding hyperlipemia pathology and hyperlipemia earlier life variations in detail, clinical symptoms, examination criteria value, people at highest risk special
Levy, the logic association between this several causes of disease and variable, ultimately form the high fat of blood to hyperlipemia illness probability Accurate Prediction
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
Property suffers from hyperlipemia probability eventually through hyperlipemia pathology Neural Network model predictive user, is carried in the way of visual effect
Awake user's instant hospitalizing, constantly revises neural network model when Neural Network model predictive is inaccurate by increasable algorithm,
To set up, for each equipment user, the neural network model training for this user, with the increase of use time, to build
The vertical 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 hyperlipemia based on increment type neural network model is predicted
Method, comprises the steps：
Step (1), acquisition hospital's hyperlipemia etiology and pathology data source and the daily monitoring data of patient, thus set up high blood
The daily data database of fat disease；
Step (2), the daily data database of hyperlipemia set up according to step (1) are off-line manner to neutral net
Model is trained, to obtain the hyperlipemia 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 hyperlipemia danger journey in the hyperlipemia pathology neural network model training in input step (2) after normalized
Degree probabilistic forecasting, obtains hyperlipemia probability results array P, corresponding for maximum probability in array P hyperlipemia is endangered by server
Dangerous degree value W sends wired home hyperlipemia care appliances to；
Step (5), hyperlipemia degree of danger value W of wired home hyperlipemia care appliances the reception server transmission
Afterwards, judge whether hyperlipemia degree of danger value W is more than or equal to 3, if greater than equal to 3, then attention device warns 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 inspection result is passed through
Wired home hyperlipemia care appliances send back server, and server judges whether inspection result is correct, if inspection result
Mistake, then explanation hyperlipemia pathology Neural Network model predictive is inaccurate, if inspection result is correct, hyperlipemia is described
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, to hyperlipemia 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 hyperlipemia daily data database table and is 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 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 iterations is gradually successively decreased, and span is [01], 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 lattice of the object information of server by wired home hyperlipemia care appliances
Formula is：{ the hyperlipemia degree of danger value of the actual judgement of doctor }, server, after receiving object information, judges inspection result
Whether correct.
Further, the increasable algorithm carrying out dynamic corrections to hyperlipemia 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
Mean value 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), hyperlipemia probability results array P obtaining is one 6 dimension variable, and its 6 dimension becomes
Amount is respectively 6 hyperlipemia degree of danger probability, the corresponding high blood of maximum probability in 6 hyperlipemia degree of danger probability
Fat disease degree of danger value W sends wired home hyperlipemia care appliances to；In step (5), when hyperlipemia degree of danger
Represent preferable during value W=1, represent normal during W=2, during W=3, represent inferior health, represent dangerous during W=4, represent non-during W=5
Often dangerous, represent ill during W=6.
Further, if user includes health check-up by other means and checks oneself, learn that oneself has suffered from hyperlipemia, and intelligence
The attention device of energy family hyperlipemia care appliances does not warn then it represents that wired home hyperlipemia care appliances judge to be forbidden
Really, now execution step (6)～(7), wired home hyperlipemia care appliances are sent to object information on server.
Present invention also offers a kind of forecasting system of described hyperlipemia Forecasting Methodology, including intelligent monitoring device, intelligence
Energy device data acquisition device, server and wired home hyperlipemia care appliances, described intelligent monitoring device and described intelligence
Device data acquisition device is connected, and described smart machine data acquisition unit is passed through communication device one and led to described server network
News, described wired home hyperlipemia care appliances pass through communication device two and described server network communication.
Further, described wired home hyperlipemia 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 hyperlipemia pathology
With hyperlipemia earlier life variations in detail, clinical symptoms, examination criteria value, people at highest risk's feature, between this several causes of disease
Logic association and variable, ultimately form the hyperlipemia pathology neural network model to hyperlipemia illness probability Accurate Prediction,
The present invention passes through to gather user's daily life data, and the periodicity of its data of active analysis, regularity are eventually through hyperlipemia
Pathology Neural Network model predictive user suffers from hyperlipemia degree of danger probability, reminds user instant in the way of visual effect
Seek medical advice and prevent.
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 hyperlipemia Forecasting Methodology based on increment type neural network model that the present invention provides, bag
Include following steps：
Step (1), acquisition hospital's hyperlipemia etiology and pathology data source and the daily monitoring data of patient, thus set up high blood
The daily data database of fat disease；
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 hyperlipemia set up according to step (1) are off-line manner to neutral net
Model is trained, to obtain the hyperlipemia 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 hyperlipemia dangerous in the hyperlipemia pathology neural network model training in input step (2) after normalized
Degree probabilistic forecasting, obtains hyperlipemia probability results array P, and server is by corresponding for maximum probability in array P hyperlipemia
Degree of danger value W sends wired home hyperlipemia care appliances to；
Wherein in step (4), hyperlipemia probability results array P obtaining is one 6 dimension variable, and its 6 dimension variable divides
Not Wei 6 hyperlipemia degree of danger probability, the corresponding hyperlipemia of maximum probability in 6 hyperlipemia degree of danger probability
Degree of danger value W sends wired home hyperlipemia care appliances to.
Step (5), hyperlipemia degree of danger value W of wired home hyperlipemia care appliances the reception server transmission
Afterwards, judge whether hyperlipemia degree of danger value W is more than or equal to 3, if greater than equal to 3, then attention device warns to remind user,
If less than 3, then attention device does not warn；
Wherein in step (5), represent preferable when hyperlipemia degree of danger value W=1, during W=2, represent normal, W=
Represent inferior health when 3, represent dangerous during W=4, represent abnormally dangerous during W=5, during W=6, represent ill.I.e. the present invention is by high blood
Fat disease 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 hyperlipemia care appliances send back server, and server judges whether inspection result is correct, if inspection result
Mistake, then explanation hyperlipemia pathology Neural Network model predictive is inaccurate, if inspection result is correct, hyperlipemia is described
Pathology Neural Network model predictive is accurate；
The form that wherein inspection result is sent back the object information of server by wired home hyperlipemia care appliances is：
{ the hyperlipemia degree of danger value of the actual judgement of doctor }, whether server, after receiving object information, judges inspection result
Correctly.
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, to hyperlipemia disease
Reason neural network model carries 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 hyperlipemia degree of danger probability), extracts 400000 records from hyperlipemia daily data database table and carries out
Training, every record is 21 dimensional vectors, all data before use first through normalized so as to numerical value is in [0,1] area
Between, 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 iterations is gradually successively decreased, and span is [01], 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 hyperlipemia 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
Mean value 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 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 hyperlipemia, and wired home is high
The attention device of blood fat disease care appliances do not warn then it represents that wired home hyperlipemia care appliances judge inaccurate, now
Execution step (6)～(7), wired home hyperlipemia care appliances are sent to object information on server.
Present invention also offers a kind of forecasting system of described hyperlipemia Forecasting Methodology, including intelligent monitoring device, intelligence
Energy device data acquisition device, server and wired home hyperlipemia care appliances, described intelligent monitoring device and described intelligence
Device data acquisition device is connected, and described smart machine data acquisition unit is passed through communication device one and led to described server network
News, described wired home hyperlipemia care appliances pass through communication device two and described server network communication.
It is provided with attention device on the described wired home hyperlipemia 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 by neural network model train predict a large amount of patient in hospital pathological data, find hyperlipemia pathology with
Hyperlipemia earlier life variations in detail, clinical symptoms, examination criteria value, people at highest risk's feature, patrolling between this several causes of disease
Collect association and variable, ultimately form the hyperlipemia pathology neural network model to hyperlipemia illness probability Accurate Prediction, this
By gathering user's daily life data, the periodicity of its data of active analysis, regularity are eventually through hyperlipemia disease for invention
Reason Neural Network model predictive user suffers from hyperlipemia probability, reminds user's instant hospitalizing and pre- in the way of visual effect
Anti-.
All data of the present invention preserve to server, can significantly save calculating cost, hardware configuration is low, thus selling
Valency 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 characteristics being shown during hyperlipemia morbidity also can be different.
Therefore conventional is not high by the method accuracy rate of neural network prediction hyperlipemia.The present invention is directed to each equipment user and sets up
Train the neural network model for this user, running after a period of time, by producing to measure nerve is being made to this user
Network Prediction Model, accuracy rate is greatly improved.
When neural network model is judged by accident, error message be will be feedbacked to service by wired home hyperlipemia care appliances
Device, for this user's dynamic corrections neural network model, when similar characteristics data in this user next, will not miss again
Sentence.Therefore, with the increase of use time, the judgement of the wired home hyperlipemia care appliances of the present invention will be more and more accurate
Really.
General 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 specification 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 hyperlipemia Forecasting Methodology based on increment type neural network model is it is characterised in that comprise the steps：
Step (1), obtain hospital hyperlipemia and cure the disease etiology and pathology data source and the daily monitoring data of patient, thus setting up high blood
The daily data database of fat disease；
Step (2), the daily data database of hyperlipemia set up according to step (1) are off-line manner to neural network model
It is trained, to obtain the hyperlipemia 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 hyperlipemia degree of danger general in the hyperlipemia pathology neural network model training in input step (2) after change process
Rate predict, obtain hyperlipemia probability results array P, server by corresponding for maximum probability in array P hyperlipemia danger journey
Angle value W sends wired home hyperlipemia care appliances to；
After step (5), hyperlipemia degree of danger value W of wired home hyperlipemia care appliances the reception server transmission, sentence
Whether disconnected hyperlipemia degree of danger value W is more than or equal to 3, and if greater than equal to 3, then attention device warns 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 hyperlipemia care appliances send back server, and server judges whether inspection result is correct, if inspection result mistake,
Then explanation hyperlipemia pathology Neural Network model predictive is inaccurate, if inspection result is correct, hyperlipemia pathology is described
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 hyperlipemia pathology nerve
Network model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
2. a kind of hyperlipemia Forecasting Methodology based on increment type neural network model according to claim 1, its feature
It is, 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 high fat of blood
Extract k bar record in disease daily data database table to be trained, every record is a n-dimensional vector, all data are using
Through normalized so as to numerical value is interval in [0,1], then execution following steps are trained to neural network model for front elder generation：
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 learning rate, it is as the function that the increase of iterations is gradually successively decreased, and span is [0 1], through many experiments
Choosing Optimal learning efficiency is 0.71；DjIt is the distance of neuron j and triumph neuron；σ (t) is as the function that the time successively decreases；
Iteration all input n-dimensional vectors is input in neural network model and is trained each time, when the iteration time reaching regulation
After number, neural network model training terminates.
3. a kind of hyperlipemia Forecasting Methodology based on increment type neural network model according to claim 1, its feature
It is, the form that inspection result is sent back the object information of server by wired home hyperlipemia care appliances is：{ doctor is real
The hyperlipemia degree of danger value that border judges }, server, after receiving object information, judges whether inspection result is correct.
4. a kind of hyperlipemia Forecasting Methodology based on increment type neural network model according to claim 1, its feature
It is, the increasable algorithm carrying out dynamic corrections to hyperlipemia 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 new
The subnet 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) it is re-started
Training, until neural network model no longer produces new neuron and new layering, whole training terminates.
5. a kind of hyperlipemia Forecasting Methodology based on increment type neural network model according to claim 1, its feature
It is, in step (4), hyperlipemia probability results array P obtaining is one 6 dimension variable, and its 6 dimension variable is respectively 6
Hyperlipemia degree of danger probability, maximum probability corresponding hyperlipemia danger journey in 6 hyperlipemia degree of danger probability
Angle value W sends wired home hyperlipemia care appliances to；In step (5), the table when hyperlipemia degree of danger value W=1
Show ideal, during W=2, represent normal, during W=3, represent inferior health, represent dangerous during W=4, during W=5, represent abnormally dangerous, W=6
When represent ill.
6. a kind of hyperlipemia Forecasting Methodology based on increment type neural network model according to claim 1, its feature
It is, if user includes health check-up by other means and checks oneself, learn that oneself has suffered from hyperlipemia, and wired home high fat of blood
The attention device of disease care appliances does not warn then it represents that wired home hyperlipemia care appliances judge inaccurate, now executes
Step (6)～(7), wired home hyperlipemia care appliances are sent to object information on server.
7. a kind of forecasting system of hyperlipemia Forecasting Methodology described in employing claim 1～7 is it is characterised in that include intelligence
Monitoring device, smart machine data acquisition unit, server and wired home hyperlipemia 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 hyperlipemia care appliances pass through communication device two and described server network communication.
8. according to claim 8 the forecasting system of hyperlipemia Forecasting Methodology it is characterised in that the high blood of described wired home
It is provided with attention device on fat disease care appliances.
9. according to claim 8 the forecasting system of hyperlipemia Forecasting Methodology it is characterised in that described intelligent monitoring device
Claim including Intelligent worn device, Intelligent water cup, Intelligent weight, intelligent closestool and Intelligent light sensing equipment.
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