CN106384013A - Incremental neural network model-based type-II diabetes prediction method and prediction system - Google Patents
Incremental neural network model-based type-II diabetes prediction method and prediction system Download PDFInfo
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Abstract
The invention discloses an incremental neural network model-based type-II diabetes prediction method. The method comprises the following steps of establishing a type-II diabetes daily data database; training a neural network model; acquiring daily life data, sending the daily life data to a server, and storing the daily life data in a user daily data record table; extracting day data in the user daily data record table to form an n-dimensional vector, performing normalization processing, and inputting the data to a type-II diabetes pathologic neural network model to perform type-II diabetes probability prediction; judging whether a type-II diabetes probability value is greater than 0.5 or not by an intelligent household type-II diabetes nursing device; if it is judged that a user suffers from type-II diabetes, enabling the user to go to a hospital for examination, transmitting an examination result back to the server through the intelligent household type-II diabetes nursing device, and judging whether the examination result is correct or not by the server; and when the examination result is wrong, executing an incremental algorithm and performing dynamic correction on the neural network model. The method 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 II type glycosuria based on increment type neural network model
Disease forecasting method and prognoses system.
Background technology
Currently domestic each health management system arranged be respectively provided with type ii diabetes prediction and evaluation, its use prediction mode be data
Coupling.Its principle is that by system matches fixed data and then personal lifestyle data entry system is shown ill probability.But due to
The complexity of human body and disease, unpredictability, in the form of expression with information for the bio signal, Changing Pattern (Self-variation
Change with after medical intervention) on, it is detected and signal representation, the data of acquisition and the analysis of information, decision-making etc. are many
All there is extremely complex non-linear relationship in aspect.So can only be the data examination of blindness using traditional Data Matching, no
Method judges the logic association and variable between data and data, and the codomain deviation obtaining is big, causes the specificity of system prediction
Very poor, domestic health management system arranged effectively Accurate Prediction cannot be carried out to personal diabetes so current.
Most of before this is all using BP neural network model to type ii diabetes prediction, but when new detection data is produced
It is necessary to train neural network model again when raw, operation efficiency is extremely low.And after system user scale increases, clothes
Business 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
Type ii diabetes Forecasting Methodology and prognoses system, the present invention by neural network model train predict a large amount of patient in hospital pathology
Data, finds type ii diabetes pathology and type ii diabetes earlier life variations in detail, clinical symptoms, examination criteria value, high-risk
Crowd characteristic, the logic association between this several causes of disease and variable, ultimately form to type ii diabetes illness probability Accurate Prediction
Type ii diabetes pathology neural network model, the present invention pass through gather user's daily life data, its data of active analysis
Periodically, regular suffer from diabetes probability, with vision eventually through type ii diabetes pathology Neural Network model predictive user
The mode of effect reminds user's instant hospitalizing, constantly revises god when Neural Network model predictive is inaccurate by increasable algorithm
Through network model, to set up, for each equipment user, the neural network model training for this user, with use time
Increase, to set up 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 type ii diabetes based on increment type neural network model are pre-
Survey method, comprises the steps：
Step (1), acquisition hospital's type ii diabetes etiology and pathology data source and the daily monitoring data of patient, thus set up II
The daily data database of patients with type Ⅰ DM；
Step (2), the daily data database of type ii diabetes set up according to step (1) are off-line manner to nerve net
Network model is trained, to obtain the type ii diabetes 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 diabetes probability pre- in the type ii diabetes pathology neural network model training in input step (2) after normalized
Survey, obtain diabetes probability, server sends diabetes probability to wired home diabetes care equipment；
After step (5), the diabetes probability of wired home diabetes care equipment the reception server transmission, judge diabetes
Whether probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained type ii diabetes, attention device warns to remind use
Family, if less than 0.5, is then judged to that this user does not obtain type ii diabetes；
Step (6), when user is judged to type ii diabetes, user voluntarily removes examination in hospital, and by inspection result
Server is sent back by wired home diabetes care equipment, server judges whether inspection result is correct, if checking knot
Fruit mistake, then explanation type ii diabetes pathology Neural Network model predictive is inaccurate, if inspection result is correct, II type is described
Diabetes 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 type ii diabetes disease
Reason 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 type ii diabetes daily data database table and is trained, every record is a n-dimensional vector,
All data before use first through normalized so as to numerical value is interval in [0,1], then execution following steps are to neutral net
Model 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 iterationses is gradually successively decreased, and span is [0 1], through multiple
It is 0.62 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 form of the object information of server by wired home diabetes care equipment
For：{ checking whether correct, blood glucose value }, server, after receiving object information, judges whether inspection result is correct.
Further, the increasable algorithm carrying out dynamic corrections to type ii diabetes 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, daily monitoring data be 12 item data, its 12 item data be body temperature, heart beating, heart rate, body fat, appetite,
The amount of drinking water, frequency of drinking water, blood pressure, body weight, the length of one's sleep, the quality length of one's sleep and distance of walking daily.
Further, if user includes health check-up by other means and checks oneself, learn that oneself has suffered from type ii diabetes, and
The attention device of wired home diabetes care equipment does not warn then it represents that wired home diabetes care equipment judges to be forbidden
Really, now execution step (6)～(7), wired home diabetes care equipment is sent to object information on server.
Present invention also offers a kind of prognoses system of described type ii diabetes Forecasting Methodology, including intelligent monitoring device,
Smart machine data acquisition unit, server and wired home diabetes care equipment, 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 diabetes care equipment passes through communication device two and described server network communication.
Further, described wired home diabetes care equipment is 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 type ii diabetes disease
Reason and type ii diabetes earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, this several causes of disease it
Between logic association and variable, ultimately form to type ii diabetes illness probability Accurate Prediction type ii diabetes pathology nerve net
Network model, 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 II
Patients with type Ⅰ DM pathology Neural Network model predictive user suffers from diabetes probability, immediately reminds user in the way of visual effect
Doctor and prevention.
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 type ii diabetes Forecasting Methodology based on increment type neural network model that the present invention provides,
Comprise the steps：
Step (1), acquisition hospital's type ii diabetes etiology and pathology data source and the daily monitoring data of patient, thus set up II
The daily data database of patients with type Ⅰ DM；
Wherein daily monitoring data is 12 item data, and its 12 item data is body temperature, heart beating, heart rate, body fat, appetite, drinks water
Amount, frequency of drinking water, blood pressure, body weight, the length of one's sleep, the quality length of one's sleep and distance of walking daily, the present invention is built with 12 item data
Vertical 12 dimensional vectors；
Step (2), the daily data database of type ii diabetes set up according to step (1) are off-line manner to nerve net
Network model is trained, to obtain the type ii diabetes 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 12 dimensional vectors, and to 12 dimensional vectors
Carry out diabetes probability in the type ii diabetes pathology neural network model training in input step (2) after doing normalized
Prediction, obtains diabetes probability, server sends diabetes probability to wired home diabetes care equipment；
After step (5), the diabetes probability of wired home diabetes care equipment the reception server transmission, judge diabetes
Whether probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained type ii diabetes, attention device warns to remind use
Family, if less than 0.5, is then judged to that this user does not obtain type ii diabetes；
Step (6), when user is judged to type ii diabetes, user voluntarily removes examination in hospital, and by inspection result
Server is sent back by wired home diabetes care equipment, server judges whether inspection result is correct, if checking knot
Fruit mistake, then explanation type ii diabetes pathology Neural Network model predictive is inaccurate, if inspection result is correct, II type is described
Diabetes pathology Neural Network model predictive is accurate；
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 type ii diabetes
Pathology 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 12 nodes, and hidden layer number is 25, and output layer is 1 node
(i.e. the probability of type ii diabetes), extracts 400000 records from type ii diabetes daily data database table and is trained,
Every record is 12 dimensional vectors, all data before use first through normalized so as to numerical value is interval in [0,1], so
Execution following steps are trained to neural network model afterwards：1) one 12 dimensional vector of input, to neural network model, calculate god
Arrive through weight vector all of in network model
The distance of this input 12 dimensional 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 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 0.62 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.
Inspection result is sent back the form of the object information of server by the wired home diabetes care equipment of the present invention
For：{ checking whether correct, blood glucose value }, server, after receiving object information, judges whether inspection result is correct.
The increasable algorithm carrying out dynamic corrections to type ii diabetes 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 the user of the present invention includes health check-up by other means and checks oneself, learn that oneself has suffered from type ii diabetes, and
The attention device of wired home diabetes care equipment does not warn then it represents that wired home diabetes care equipment judges to be forbidden
Really, now execution step (6)～(7), wired home diabetes care equipment is sent to object information on server.
Present invention also offers a kind of prognoses system of described type ii diabetes Forecasting Methodology, including intelligent monitoring device,
Smart machine data acquisition unit, server and wired home diabetes care equipment, 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 diabetes care equipment passes through communication device two and described server network communication.
It is provided with attention device on the described wired home diabetes care equipment 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 type ii diabetes pathology
With type ii diabetes earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, between this several causes of disease
Logic association and variable, ultimately form to type ii diabetes illness probability Accurate Prediction type ii diabetes pathology neutral net
Model, 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 II type
Diabetes pathology Neural Network model predictive user suffers from diabetes probability, reminds user's instant hospitalizing in the way of visual effect
And prevention.
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 characteristicses being shown during onset diabetes also can be different.Cause
This is not conventional high by the method accuracy rate of neural network prediction diabetes.The present invention is directed to each equipment user and sets up training
Go out the neural network model for this user, running after a period of time, by producing to measure neutral net is being made to this user
Forecast model, accuracy rate is greatly improved.
When neural network model is judged by accident, error message be will be feedbacked to server by wired home diabetes care equipment,
For this user's dynamic corrections neural network model, when similar characteristics data in this user next, will not judge by accident again.Cause
This, with the increase of use time, the judgement of the wired home diabetes care equipment of the present invention will be 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 type ii diabetes Forecasting Methodology based on increment type neural network model is it is characterised in that comprise the steps：
Step (1), acquisition hospital's type ii diabetes etiology and pathology data source and the daily monitoring data of patient, thus set up II type sugar
Urinate the daily data database of disease；
Step (2), the daily data database of type ii diabetes set up according to step (1) are off-line manner to neutral net mould
Type is trained, to obtain the type ii diabetes 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 diabetes probabilistic forecasting in the type ii diabetes pathology neural network model training in input step (2) after change process,
Obtain diabetes probability, server sends diabetes probability to wired home diabetes care equipment；
After step (5), the diabetes probability of wired home diabetes care equipment the reception server transmission, judge diabetes probability
Whether value is more than 0.5, if greater than 0.5, is then judged to that this user obtained type ii diabetes, attention device warns to remind user,
If less than 0.5, then it is judged to that this user does not obtain type ii diabetes；
Step (6), when user is judged to type ii diabetes, user voluntarily removes examination in hospital, and inspection result is passed through
Wired home diabetes care equipment sends back server, and server judges whether inspection result is correct, if inspection result is wrong
By mistake, then explanation type ii diabetes pathology Neural Network model predictive is inaccurate, if inspection result is correct, II type glycosuria 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 increment
In tables of data, when the record quantity in incremental data table is more than h bar, execute increasable algorithm, to type ii diabetes pathology god
Carry out dynamic corrections through network model；
Step (8), repeat step (3)～(7).
2. a kind of type ii diabetes Forecasting Methodology based on increment type neural network model according to claim 1, it is special
Levy and be, 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 II type
Extract k bar record in diabetes daily data database table to be trained, every record is a n-dimensional vector, and all data exist
Using front elder generation through normalized so as to numerical value is interval in [0,1], then execution following steps are instructed to neural network model
Practice：
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 iterationses is gradually successively decreased, and span is [0 1], through many experiments
Choosing Optimal learning efficiency is 0.62；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 type ii diabetes Forecasting Methodology based on increment type neural network model according to claim 1, it is special
Levy and be, the form that inspection result is sent back the object information of server by wired home diabetes care equipment is：{ inspection is
No correct, blood glucose value }, server, after receiving object information, judges whether inspection result is correct.
4. a kind of type ii diabetes Forecasting Methodology based on increment type neural network model according to claim 1, it is special
Levy and be, the increasable algorithm carrying out dynamic corrections to type ii diabetes 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 type ii diabetes Forecasting Methodology based on increment type neural network model according to claim 1, it is special
Levy and be, daily monitoring data is 12 item data, its 12 item data is body temperature, heart beating, heart rate, body fat, appetite, the amount of drinking water, drinks
Water frequency, blood pressure, body weight, the length of one's sleep, the quality length of one's sleep and distance of walking daily.
6. a kind of type ii diabetes Forecasting Methodology based on increment type neural network model according to claim 1, it is special
Levy and be, if user includes health check-up by other means and checks oneself, learn that oneself has suffered from type ii diabetes, and wired home is sugared
The attention device of the sick care appliances of urine does not warn then it represents that wired home diabetes care equipment judges inaccurate, now executes
Step (6)～(7), wired home diabetes care equipment is sent to object information on server.
7. a kind of prognoses system of type ii diabetes Forecasting Methodology described in employing claim 1～7 is it is characterised in that include intelligence
Energy monitoring device, smart machine data acquisition unit, server and wired home diabetes care equipment, 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 diabetes care equipment passes through communication device two and described server network communication.
8. according to claim 8 the prognoses system of type ii diabetes Forecasting Methodology it is characterised in that described wired home is sugared
It is provided with attention device on the sick care appliances of urine.
9. according to claim 8 the prognoses system of type ii diabetes Forecasting Methodology it is characterised in that described intelligent monitoring sets
Standby include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool and Intelligent light sensing equipment.
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