CN106295239A - A kind of fatty liver Forecasting Methodology based on increment type neural network model and prognoses system - Google Patents

Abstract

The invention discloses a kind of fatty liver Forecasting Methodology based on increment type neural network model, comprise the steps: to set up the daily data database of fatty liver；Neural network model is trained；Gather daily life data to send to server, preserve to the daily data logger of user；The daily data logger of user extracts data on the same day, forms n-dimensional vector, input in fatty liver pathology neural network model after doing normalized and carry out fatty liver probabilistic forecasting；Wired home fatty liver care appliances judges that whether fatty liver probit is more than 0.5；User is judged to fatty liver, and user removes examination in hospital voluntarily, and by wired home fatty liver care appliances, inspection result is sent back server, and server judges to check that result is the most correct；Perform increasable algorithm when checking result mistake, neural network model is carried out dynamic corrections.The present invention predicts accurately, makes neural network model to measure for each user.

Description

A kind of fatty liver Forecasting Methodology based on increment type neural network model and prognoses system

Technical field

The invention belongs to field of medical technology, particularly relate to a kind of fatty liver based on increment type neural network model pre- Survey method and prognoses system.

Background technology

Present Domestic is each health management system arranged is respectively provided with fatty liver prediction and evaluation, and its prediction mode used is data Join.Its principle is that by system matches fixed data, then personal lifestyle data entry system is shown ill probability.But due to people Body and the complexity of disease, unpredictability, in the form of expression of bio signal and information, Changing Pattern (Self-variation with Change after medical intervention) on, it being detected and signal representation, the data of acquisition and the analysis of information, decision-making etc. are all in many ways All there is extremely complex non-linear relationship in face.So using traditional Data Matching can only be data examination blindly, it is impossible to Judging the logic association between data and data and variable, the codomain deviation obtained is big, causes the specificity ten of system prediction Point poor, domestic health management system arranged effectively individual fatty liver cannot be carried out Accurate Prediction so current.

Major part is all to use BP neural network model to fatty liver prediction before this, but when new detection data generation Time, it is necessary to again training neural network model, operation efficiency is extremely low.And after system user scale increases, server Will be unable to complete in time training mission.

Summary of the invention

The purpose of the present invention is that and overcomes the deficiencies in the prior art, it is provided that a kind of based on increment type neural network model Fatty liver Forecasting Methodology and prognoses system, the present invention by neural network model training predict a large amount of patient in hospital pathology numbers According to, find fatty liver pathology and fatty liver earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, this Logic association between several the causes of disease and variable, ultimately form the fatty liver pathology to Fatty Liver Diseases probability Accurate Prediction neural Network model, the present invention by gathering user's daily life data, the periodicity of its data of active analysis, regular eventually through The probability that suffers from fatty liver of fatty liver pathology Neural Network model predictive user, reminds user instant just in the way of visual effect Doctor, constantly revises neural network model, to set for each when Neural Network model predictive is inaccurate by increasable algorithm Standby user is set up and is trained the neural network model for this user, along with the increase of the time of use, to set up this customer volume The neural network model that body is customized, accuracy rate is greatly improved.

To achieve these goals, the invention provides a kind of fatty liver prediction side based on increment type neural network model Method, comprises the steps:

Step (1), acquisition hospital fatty liver etiology and pathology data source and patient's daily monitoring data, thus set up fatty liver Daily data database；

Step (2), the daily data database of fatty liver set up according to step (1) are off-line manner to neutral net mould Type is trained, to obtain the fatty liver pathology neural network model trained；

Step (3), by intelligent monitoring device, the daily life data of user are acquired, and the daily life that will gather Live data sends to server, and the daily life data of user are preserved to the daily data logger of user by server；

Step (4), from the daily data logger of user, extract data on the same day, form n-dimensional vector, and n-dimensional vector is done The fatty liver pathology neural network model trained in input step (2) after normalized carries out fatty liver probabilistic forecasting, Obtaining fatty liver probability, fatty liver probability is sent to wired home fatty liver care appliances by server；

After step (5), wired home fatty liver care appliances receive the fatty liver probability that server transmits, it is judged that fatty liver Whether probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained fatty liver, and attention device warns to remind user, If less than 0.5, then it is judged to that this user does not obtain fatty liver；

Step (6), when user is judged to fatty liver, user removes examination in hospital voluntarily, and inspection result is passed through Wired home fatty liver care appliances sends back server, and server judges to check that result is the most correct, if checking that result is wrong By mistake, then explanation fatty liver pathology Neural Network model predictive is inaccurate, if checking that result is correct, then and explanation fatty liver pathology god Through network model's prediction accurately；

Step (7), when checking result mistake, extract from the daily data logger of user the record in m days preserve to In incremental data table, when the record quantity in incremental data table is more than h bar, perform increasable algorithm, to fatty liver pathology god Dynamic corrections is carried out through network model；

Step (8), repetition 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 fatty liver daily data database table and is trained, and every record is a n-dimensional vector, all Data are before use first through normalized so that it is numerical value is interval in [0,1], then perform following steps to neural network model It is trained:

1) one n-dimensional vector of input is to neural network model, calculates all of weight vector in neural network model defeated to this Entering the distance of n-dimensional vector, closest neuron is triumph neuron, and its computing formula is as follows:

$\left|\right|x_i-W_k\left|\right|=\underset{j}{\min }\left(\right||x_i-W_j|\left|\right);$

Wherein: W_kIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；

2) adjusting the weight vector of neuron in triumph neuron and triumph neuron field, formula is as follows:

Wherein: W_jT () is neuron；W_j(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck Territory；α (t) is learning rate, and it is as the function that the increase of iterations is gradually successively decreased, and span is [0 1], through repeatedly It is 0.62 that Optimal learning efficiency is chosen in experiment；D_jIt it is the distance of neuron j and triumph neuron；σ (t) is as the letter that the time successively decreases Number；All input n-dimensional vectors all are input in neural network model be trained by iteration each time, when the iteration reaching regulation After number of times, neural network model training terminates.

Further, wired home fatty liver care appliances will check that result sends back the form of the object information of server For: { checking whether correct, blood glucose value }, server is after receiving object information, it is judged that check that result is the most correct.

Further, the increasable algorithm that fatty liver pathology neural network model carries out dynamic corrections is:

Vectorial for every in incremental data table V{V₁,V₂,…,V_n, it is sent in neural network model learning function enter Row study, learning procedure is as follows:

1) first each to output layer weight vector is composed little random number and does normalized, then utilizes input mode vector V Meansigma methods Avg (V), be initialized as in neural network model the 0th layer the weights of unique neuron, and be set to nerve of winning Unit, calculates its quantization error QE；

2) from the neuron of the 0th layer, expand out 2 × 2 structures SOM, and its level identities Layer is set to 1；

3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, the power of these 4 neurons is initialized Value；The input vector set Ci of i-th neuron is set to sky, and main label is set to the main label ratio r of NULL, neuron i_i 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) from VX, select a vectorial VX_iDo following judgement:

If VX_iFor the data of not tape label, then calculating the Euclidean distance of it and each neuron, chosen distance is the shortest Neuron is as triumph neuron；

If VX_iFor the data of tape label, then select main label and VX_iLabel is identical and r_iThe neuron work that value is maximum For triumph neuron, update this triumph neuron main label；

If can not find main label and VX_iThe identical neuron of label, then find and VX_iClosest neuron i makees For triumph neuron；

5) weights of neuron in triumph neuron and neighborhood thereof are adjusted, update the vector set W=W ∪ that wins {VX_i, calculate the main label of triumph neuron, main label ratio r_iWith comentropy E_iIf. the most predetermined frequency of training, then Go to step 4)；

6) calculate adjusted after this neural network model in quantization error QE of each neuron_i, neuronal messages entropy E_i With the average quantization error MQE of subnet, formula is as follows:

${\mathrm{QE}}_i=\underset{X_j\∈C_i}{\mathrm{\Σ}}\left\{\right||W_i-X_j|\left|\right\};$

Wherein: W_iFor the weight vector of neuron i, C_iFor being mapped to the set that all input vectors of neuron i are constituted；

$E_i={\mathrm{\Σ}}_{i\∈T}(-lb\frac{n_i}{m})\×\frac{n_i}{m};$

Wherein: n_iRepresenting 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 kind set on neuron；

Then judge:

If QE × threshold value q of MQE > father node, wherein q=0.71, then in this SOM, insert a line neuron, turn step Rapid 4)；

If E_i> E of father node_i× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will The subnet newly grown increases in the subnet queue of Layer+1 layer；

If SOM is not inserted into the not longest subnet made new advances of new neuron, illustrate that this subnet has been trained；

7) all 2 × 2 structures SOM of the Layer+1 layer for newly expanding out, iteration operating procedure 3)～5) to it again Being trained, until neural network model no longer produces new neuron and new layering, whole training terminates.

Further, if user includes health check-up by other means and checks oneself, learn that oneself suffers from fatty liver, and intelligent The attention device of family's fatty liver care appliances does not warn, then it represents that wired home fatty liver care appliances judges inaccurate, this Shi Zhihang step (6)～(7), wired home fatty liver care appliances is sent to object information on server.

Present invention also offers the prognoses system of a kind of described fatty liver Forecasting Methodology, including intelligent monitoring device, intelligence Device data acquisition device, server and wired home fatty liver care appliances, described intelligent monitoring device and described smart machine Data acquisition unit is connected, and described smart machine data acquisition unit is by communication device one and described server network communication, institute State wired home fatty liver care appliances by communication device two and described server network communication.

Further, described wired home fatty liver care appliances is provided with attention device.

Further, described intelligent monitoring device include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligence horse Bucket and Intelligent light sensing equipment.

Beneficial effects of the present invention:

1, the present invention by neural network model training predict a large amount of patient in hospital pathological data, find fatty liver pathology with Fatty liver earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, the logic between these several the causes of disease Association and variable, ultimately form the fatty liver pathology neural network model to Fatty Liver Diseases probability Accurate Prediction, and the present invention is led to Crossing collection user's daily life data, the periodicity of its data of active analysis, regularity are eventually through fatty liver pathology nerve net The probability that suffers from fatty liver of network model prediction user, reminds user's instant hospitalizing and prevention in the way of visual effect.

2, constantly revise neural network model when Neural Network model predictive is inaccurate by increasable algorithm, with for Each equipment user sets up and trains the neural network model for this user, along with the increase of the time of use, to set up this The neural network model that user makes to measure, accuracy rate is greatly improved.

Accompanying drawing explanation

In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will to embodiment or In description of the prior art, the required accompanying drawing used is briefly described, it should be apparent that, the accompanying drawing in describing below is only Some embodiments of the present invention, for those of ordinary skill in the art, on the premise of not paying creative work, also may be used To obtain other accompanying drawing according to these accompanying drawings.

Fig. 1 is the flow chart of the embodiment of the present invention.

Detailed description of the invention

Below in conjunction with the accompanying drawings invention is further illustrated, but be not limited to the scope of the present invention.

Embodiment

As it is shown in figure 1, a kind of based on increment type neural network model the fatty liver Forecasting Methodology that the present invention provides, including Following steps:

Step (1), acquisition hospital fatty liver etiology and pathology data source and patient's daily monitoring data, thus set up fatty liver Daily data database；

The most daily monitoring data are 21 item data, and its 21 item data is the age, sex, heart rate, greasy oil degree of taking food, high Danger condition, liver function, body weight, to drink (every day), temperature, humidity, 21 item data such as air quality index, the present invention is with 21 item numbers According to setting up 21 dimensional vectors；

Step (2), the daily data database of fatty liver set up according to step (1) are off-line manner to neutral net mould Type is trained, to obtain the fatty liver pathology neural network model trained；

Step (3), by intelligent monitoring device, the daily life data of user are acquired, and the daily life that will gather Live data sends to server, and the daily life data of user are preserved to the daily data logger of user by server；

Step (4), from the daily data logger of user, extract data on the same day, form 21 dimensional vectors, and to 21 dimensional vectors The fatty liver pathology neural network model trained in input step (2) after doing normalized carries out fatty liver probability pre- Surveying, obtain fatty liver probability, fatty liver probability is sent to wired home fatty liver care appliances by server；

After step (5), wired home fatty liver care appliances receive the fatty liver probability that server transmits, it is judged that fatty liver Whether probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained fatty liver, and attention device warns to remind user, If less than 0.5, then it is judged to that this user does not obtain fatty liver；

Step (6), when user is judged to fatty liver, user removes examination in hospital voluntarily, and inspection result is passed through Wired home fatty liver care appliances sends back server, and server judges to check that result is the most correct, if checking that result is wrong By mistake, then explanation fatty liver pathology Neural Network model predictive is inaccurate, if checking that result is correct, then and explanation fatty liver pathology god Through network model's prediction accurately；

Step (7), when checking result mistake, extract from the daily data logger of user the record in 7 days preserve to In incremental data table, when the record quantity in incremental data table is more than 100, perform increasable algorithm, to fatty liver pathology Neural network model carries out dynamic corrections；

Step (8), repetition 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 1 node (i.e. the probability of fatty liver), extracts 400000 records from fatty liver daily data database table and is trained, every record Being 21 dimensional vectors, all data are before use first through normalized so that it is numerical value is interval in [0,1], then performs such as Neural network model is trained by lower step:

1) one 21 dimensional vector of input are to neural network model, calculate all of weight vector in neural network model defeated to this Entering the distance of 21 dimensional vectors, closest neuron is triumph neuron, and its computing formula is as follows:

$\left|\right|x_i-W_k\left|\right|=\underset{j}{\min }\left(\right||x_i-W_j|\left|\right);$

Wherein: W_kIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；

2) adjusting the weight vector of neuron in triumph neuron and triumph neuron field, formula is as follows:

Wherein: W_jT () is neuron；W_j(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck Territory；α (t) is learning rate, and it is as the function that the increase of iterations is gradually successively decreased, and span is [0 1], through repeatedly It is 0.62 that Optimal learning efficiency is chosen in experiment；D_jIt it is the distance of neuron j and triumph neuron；σ (t) is as the letter that the time successively decreases Number；All input n-dimensional vectors all are input in neural network model be trained by iteration each time, when the iteration reaching regulation After number of times, neural network model training terminates.

The wired home fatty liver care appliances of the present invention will check that result sends back the form of the object information of server For: { checking whether correct, blood glucose value }, server is after receiving object information, it is judged that check that result is the most correct.

The increasable algorithm that fatty liver pathology neural network model carries out dynamic corrections of the present invention is:

Vectorial for every in incremental data table V{V₁,V₂,…,V_n, it is sent in neural network model learning function enter Row study, learning procedure is as follows:

1) first each to output layer weight vector is composed little random number and does normalized, then utilizes input mode vector V Meansigma methods Avg (V), be initialized as in neural network model the 0th layer the weights of unique neuron, and be set to nerve of winning Unit, calculates its quantization error QE；

2) from the neuron of the 0th layer, expand out 2 × 2 structures SOM, and its level identities Layer is set to 1；

3) for each 2 × 2 structure SOM subnet expanded out in Layer layer, the power of these 4 neurons is initialized Value；The input vector set Ci of i-th neuron is set to sky, and main label is set to the main label ratio r of NULL, neuron i_i 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) from VX, select a vectorial VX_iDo following judgement:

If VX_iFor the data of not tape label, then calculating the Euclidean distance of it and each neuron, chosen distance is the shortest Neuron is as triumph neuron；

If VX_iFor the data of tape label, then select main label and VX_iLabel is identical and r_iThe neuron work that value is maximum For triumph neuron, update this triumph neuron main label；

If can not find main label and VX_iThe identical neuron of label, then find and VX_iClosest neuron i makees For triumph neuron；

5) weights of neuron in triumph neuron and neighborhood thereof are adjusted, update the vector set W=W ∪ that wins {VX_i, calculate the main label of triumph neuron, main label ratio r_iWith comentropy E_iIf. the most predetermined frequency of training, then Go to step 4)；

6) calculate adjusted after this neural network model in quantization error QE of each neuron_i, neuronal messages entropy E_i With the average quantization error MQE of subnet, formula is as follows:

${\mathrm{QE}}_i=\underset{X_j\∈C_i}{\mathrm{\Σ}}\left\{\right||W_i-X_j|\left|\right\};$

Wherein: W_iFor the weight vector of neuron i, C_iFor being mapped to the set that all input vectors of neuron i are constituted；

$E_i={\mathrm{\Σ}}_{i\∈T}(-lb\frac{n_i}{m})\×\frac{n_i}{m};$

Wherein: n_iRepresenting 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 kind set on neuron；

Then judge:

If QE × threshold value q of MQE > father node, wherein q=0.71, then in this SOM, insert a line neuron, turn step Rapid 4)；

If E_i> E of father node_i× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will The subnet newly grown increases in the subnet queue of Layer+1 layer；

If SOM is not inserted into the not longest subnet made new advances of new neuron, illustrate that this subnet has been trained；

7) all 2 × 2 structures SOM of the Layer+1 layer for newly expanding out, iteration operating procedure 3)～5) to it again Being 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 suffers from fatty liver, and intelligent The attention device of family's fatty liver care appliances does not warn, then it represents that wired home fatty liver care appliances judges inaccurate, this Shi Zhihang step (6)～(7), wired home fatty liver care appliances is sent to object information on server.

Present invention also offers the prognoses system of a kind of described fatty liver Forecasting Methodology, including intelligent monitoring device, intelligence Device data acquisition device, server and wired home fatty liver care appliances, described intelligent monitoring device and described smart machine Data acquisition unit is connected, and described smart machine data acquisition unit is by communication device one and described server network communication, institute State wired home fatty liver care appliances by communication device two and described server network communication.

It is provided with attention device on the described wired home fatty liver care appliances of the present invention.

The described intelligent monitoring device of the present invention include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool With Intelligent light sensing equipment etc..

The present invention predicts a large amount of patient in hospital pathological data by neural network model training, finds fatty liver pathology and fat Fat liver earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, the logic between these several the causes of disease is closed Connection and variable, ultimately form the fatty liver pathology neural network model to Fatty Liver Diseases probability Accurate Prediction, and the present invention passes through Gathering user's daily life data, the periodicity of its data of active analysis, regularity are eventually through fatty liver pathology neutral net The probability that suffers from fatty liver of model prediction user, reminds user's instant hospitalizing and prevention in the way of visual effect.

All data of the present invention preserve to server, can significantly save calculating cost, and hardware configuration is low, thus sells Valency is the lowest.

The present invention carries communication device one and communication device two, by wifi from being dynamically connected the Internet, and can protect for a long time Hold online.Various intelligent monitoring devices can easily access present device by the mode such as network or bluetooth, sets in acquisition The daily life data of the monitoring of intelligent monitoring device, the data that therefore present device obtains can be automatically uploaded after standby mandate Be real-time, accurately, polynary.

Owing to everyone physical trait is different, the data characteristics shown during pathogenesis of fatty liver also can be different.Cause This is conventional the highest by the method accuracy rate of neural network prediction fatty liver.The present invention is directed to each equipment user and set up training Go out the neural network model for this user, running after a period of time, this user is being made generation to measure neutral net 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 fatty liver care appliances, For this user's dynamic corrections neural network model, when next time, similar characteristics data occurred in this user, will not judge by accident again.Cause This, along with the increase of the time of use, the judgement of the wired home fatty liver care appliances of the present invention will be more and more accurate.

The ultimate principle of the present invention, principal character and advantages of the present invention have more than been shown and described.The technology of the industry Personnel, it should be appreciated that the present invention is not restricted to the described embodiments, simply illustrating this described in above-described embodiment and description The principle of invention, the present invention also has various changes and modifications without departing from the spirit and scope of the present invention, and these become Change and improvement both falls within scope of the claimed invention.Claimed scope by appending claims and Equivalent defines.

Claims (8)

1. a fatty liver Forecasting Methodology based on increment type neural network model, it is characterised in that comprise the steps:

Step (1), obtain hospital fatty liver and cure the disease etiology and pathology data source and patient's daily monitoring data, thus set up fatty liver Daily data database；

Neural network model is entered by step (2), the daily data database of fatty liver set up according to step (1) off-line manner Row training, to obtain the fatty liver pathology neural network model trained；

Step (3), by intelligent monitoring device, the daily life data of user are acquired, and the daily life number that will gather According to sending to server, the daily life data of user are preserved to the daily data logger of user by server；

Step (4), from the daily data logger of user, extract data on the same day, form n-dimensional vector, and n-dimensional vector is done normalizing The fatty liver pathology neural network model trained in input step (2) after change process carries out fatty liver probabilistic forecasting, obtains Fatty liver probability, fatty liver probability is sent to wired home fatty liver care appliances by server；

After step (5), wired home fatty liver care appliances receive the fatty liver probability that server transmits, it is judged that fatty liver probability Whether value is more than 0.5, if greater than 0.5, is then judged to that this user obtained fatty liver, and attention device warns to remind user, if Less than 0.5, then it is judged to that this user does not obtain fatty liver；

Step (6), when user is judged to fatty liver, user removes examination in hospital voluntarily, and will check that result is by intelligence Family's fatty liver care appliances sends back server, and server judges to check that result is the most correct, if checking result mistake, then Illustrate that fatty liver pathology Neural Network model predictive is inaccurate, if checking that result is correct, then explanation fatty liver pathology nerve net Network model prediction is accurate；

Step (7), when checking result mistake, the record extracted from the daily data logger of user in m days preserves to increment In tables of data, when the record quantity in incremental data table is more than h bar, perform increasable algorithm, to fatty liver pathology nerve net Network model carries out dynamic corrections；

Step (8), repetition step (3)～(7).

A kind of fatty liver Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature exists In, 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 fatty liver day Extracting k bar record in regular data database table to be trained, every record is a n-dimensional vector, and all data are first before use Through normalized so that it is numerical value is interval in [0,1], then perform following steps and neural network model be trained:

1) one n-dimensional vector of input is 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 triumph neuron, and its computing formula is as follows:

Wherein: W_kIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；

2) adjusting the weight vector of neuron in triumph neuron and triumph neuron field, formula is as follows:

Wherein: W_jT () is neuron；W_j(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 0.62；D_jIt it is the distance of neuron j and triumph neuron；σ (t) is as the function that the time successively decreases； All input n-dimensional vectors all are input in neural network model be trained by iteration each time, when the iteration time reaching regulation After number, neural network model training terminates.

A kind of fatty liver Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature exists In, the form of the object information that inspection result sends back server is by wired home fatty liver care appliances: { just check whether Really, blood glucose value }, server is after receiving object information, it is judged that check that result is the most correct.

A kind of fatty liver Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature exists In, the increasable algorithm that fatty liver pathology neural network model carries out dynamic corrections is:

Vectorial for every in incremental data table V{V₁,V₂,…,V_n, it is sent in neural network model learning function learn Practising, learning procedure is as follows:

1) first each to output layer weight vector is composed little random number and does normalized, then utilizes the flat of input mode vector V Average Avg (V), is initialized as in neural network model the 0th layer the weights of unique neuron, and is set to triumph neuron, meter Calculate its quantization error QE；

2) from the neuron of the 0th layer, expand out 2 × 2 structures SOM, 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 these 4 neurons are initialized；Will The input vector set Ci of i-th neuron is set to sky, and main label is set to the main label ratio r of NULL, neuron i_iIt 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) from VX, select a vectorial VX_iDo following judgement:

If VX_iFor the data of not tape label, then calculate the Euclidean distance of it and each neuron, the nerve that chosen distance is the shortest Unit is as triumph neuron；

If VX_iFor the data of tape label, then select main label and VX_iLabel is identical and r_iThe neuron of value maximum is as obtaining Victory neuron, updates this triumph neuron main label；

If can not find main label and VX_iThe identical neuron of label, then find and VX_iClosest neuron i is as obtaining Victory neuron；

5) weights of neuron in triumph neuron and neighborhood thereof are adjusted, update the vector set W=W ∪ { VX that wins_i, Calculate the main label of triumph neuron, main label ratio r_iWith comentropy E_iIf. the most predetermined frequency of training, then go to step 4)；

6) calculate adjusted after this neural network model in quantization error QE of each neuron_i, neuronal messages entropy E_iAnd son The average quantization error MQE of net, formula is as follows:

Wherein: W_iFor the weight vector of neuron i, C_iFor being mapped to the set that all input vectors of neuron i are constituted；

Wherein: n_iRepresenting 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 kind set on neuron；

Then judge:

If QE × threshold value q of MQE > father node, wherein q=0.71, then in this SOM, insert a line neuron, go to step 4)；

If E_i> E of father node_i× threshold value p, wherein p=0.42, then grow one layer of new subnet from this neuron, will be new The subnet grown increases in the subnet queue of Layer+1 layer；

If SOM is not inserted into the not longest subnet made new advances of new neuron, illustrate that this subnet has been trained；

7) all 2 × 2 structures SOM of the Layer+1 layer for newly expanding 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.

A kind of fatty liver Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature exists In, if user includes health check-up by other means and checks oneself, learn that oneself suffers from fatty liver, and the nursing of wired home fatty liver The attention device of equipment does not warn, then it represents that wired home fatty liver care appliances judges inaccurate, now perform step (6)～ (7), wired home fatty liver care appliances is sent to object information on server.

6. one kind uses the prognoses system of fatty liver Forecasting Methodology described in claim 1～6, it is characterised in that include intelligence prison Control equipment, smart machine data acquisition unit, server and wired home fatty liver care appliances, described intelligent monitoring device and institute Stating smart machine data acquisition unit to be connected, described smart machine data acquisition unit is by communication device one and described server net Network communication, described wired home fatty liver care appliances is by communication device two and described server network communication.

The prognoses system of fatty liver Forecasting Methodology the most according to claim 7, it is characterised in that described wired home fatty liver Attention device it is provided with on care appliances.

The prognoses system of fatty liver Forecasting Methodology the most according to claim 7, it is characterised in that described intelligent monitoring device bag Include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool and Intelligent light sensing equipment.