CN106202986A - A kind of tonsillitis Forecasting Methodology based on increment type neural network model and prognoses system - Google Patents
A kind of tonsillitis Forecasting Methodology based on increment type neural network model and prognoses system Download PDFInfo
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- 206010044008 tonsillitis Diseases 0.000 title claims abstract description 123
- 238000003062 neural network model Methods 0.000 title claims abstract description 85
- 238000000034 method Methods 0.000 title claims abstract description 24
- 239000013598 vector Substances 0.000 claims abstract description 56
- 230000007170 pathology Effects 0.000 claims abstract description 29
- 238000012937 correction Methods 0.000 claims abstract description 8
- 239000000284 extract Substances 0.000 claims abstract description 8
- 238000007689 inspection Methods 0.000 claims abstract description 6
- 210000002569 neuron Anatomy 0.000 claims description 111
- 238000012549 training Methods 0.000 claims description 15
- 238000012806 monitoring device Methods 0.000 claims description 14
- 238000004891 communication Methods 0.000 claims description 12
- 238000013139 quantization Methods 0.000 claims description 9
- 230000006870 function Effects 0.000 claims description 7
- 210000002741 palatine tonsil Anatomy 0.000 claims description 6
- 201000010099 disease Diseases 0.000 claims description 5
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 5
- 230000036541 health Effects 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 5
- 235000011437 Amygdalus communis Nutrition 0.000 claims description 4
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- 206010061218 Inflammation Diseases 0.000 claims description 3
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- 239000008103 glucose Substances 0.000 claims description 3
- 230000004054 inflammatory process Effects 0.000 claims description 3
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- 230000002354 daily effect Effects 0.000 description 23
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- 230000007935 neutral effect Effects 0.000 description 3
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Abstract
The invention discloses a kind of tonsillitis Forecasting Methodology based on increment type neural network model, comprise the steps: to set up the daily data database of tonsillitis；Neural network model is trained；Gather daily life data to send to server, preserve to the daily data logger of user；From the daily data logger of user, extract data on the same day, form n-dimensional vector, input in tonsillitis pathology neural network model after doing normalized and carry out tonsillitis probabilistic forecasting；Wired home tonsillitis care appliances judges that whether tonsillitis probit is more than 0.5；When user is judged to tonsillitis, user removes examination in hospital voluntarily, and by wired home tonsillitis 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
Technical field
The invention belongs to field of medical technology, particularly relate to a kind of tonsillitis based on increment type neural network model
Forecasting Methodology and prognoses system.
Background technology
Present Domestic is each health management system arranged is respectively provided with tonsillitis 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 tonsillitis cannot be carried out Accurate Prediction so current.
Major part is all to use BP neural network model to tonsillitis prediction before this, but when new detection data produce
When, it is necessary to again training neural network model, operation efficiency is extremely low.And after system user scale increases, service
Device 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
Tonsillitis 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 tonsillitis pathology and tonsillitis earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group spy
Levy, the logic association between these several the causes of disease and variable, ultimately form the tonsil of probability Accurate Prediction ill to tonsillitis
Scorching pathology neural network model, the present invention is by gathering user's daily life data, the periodicity of its data of active analysis, rule
Property suffers from tonsillitis probability eventually through tonsillitis pathology Neural Network model predictive user, carries 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,
The neural network model for this user is trained, along with the increase of the time of use, to build to set up for each equipment user
The vertical neural network model making this user to measure, accuracy rate is greatly improved.
To achieve these goals, the invention provides the prediction of a kind of tonsillitis based on increment type neural network model
Method, comprises the steps:
Step (1), acquisition hospital tonsillitis etiology and pathology data source and patient's daily monitoring data, thus set up almond
Body buring sun regular data data base；
Step (2), the daily data database of tonsillitis set up according to step (1) are off-line manner to neutral net
Model is trained, to obtain the tonsillitis 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 tonsillitis pathology neural network model trained in input step (2) after normalized carries out tonsillitis probability pre-
Surveying, obtain tonsillitis probability, tonsillitis probability is sent to wired home tonsillitis care appliances by server；
After step (5), wired home tonsillitis care appliances receive the tonsillitis probability that server transmits, it is judged that flat
Whether Fructus Persicae body inflammation probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained tonsillitis, and attention device warns to carry
Wake up user, if less than 0.5, is then judged to that this user does not obtain tonsillitis；
Step (6), when user is judged to tonsillitis, user removes examination in hospital voluntarily, and inspection result is led to
Crossing wired home tonsillitis care appliances and send back server, server judges to check that result is the most correct, if checking knot
Really mistake, then explanation tonsillitis pathology Neural Network model predictive is inaccurate, if checking that result is correct, then tonsil is described
Scorching pathology Neural Network model predictive is accurate；
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 tonsillitis pathology
Neural network model carries out dynamic corrections；
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 tonsillitis daily data database table and is trained, and every record is a n-dimensional vector, institute
There are data before use first through normalized so that it is numerical value is interval in [0,1], then perform following steps to neutral net mould
Type 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:
Wherein: WkIt 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: WjT () is neuron；Wj(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；DjIt 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 tonsillitis care appliances will check that result sends back the lattice of the object information of server
Formula is: { checking whether correct, blood glucose value }, and server is after receiving object information, it is judged that check that result is the most correct.
Further, the increasable algorithm that tonsillitis pathology neural network model carries out dynamic corrections is:
Vectorial for every in incremental data table V{V1,V2,…,Vn, 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 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) from VX, select a vectorial VXiDo following judgement:
If VXiFor 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 VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe neuron work that value is maximum
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiThe identical neuron of label, then find and VXiClosest 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
{VXi, calculate the main label of triumph neuron, main label ratio riWith comentropy EiIf. 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 neuroni, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows:
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
Wherein: niRepresenting 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 Ei> 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 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 and oneself have suffered from tonsillitis, and intelligence
The attention device of energy family tonsillitis care appliances does not warn, then it represents that wired home tonsillitis care appliances judges inaccurate
Really, now performing step (6)～(7), wired home tonsillitis care appliances is sent to object information on server.
Present invention also offers the prognoses system of a kind of described tonsillitis Forecasting Methodology, including intelligent monitoring device, intelligence
Energy device data acquisition device, server and wired home tonsillitis care appliances, described intelligent monitoring device and described intelligence
Device data acquisition device is connected, and described smart machine data acquisition unit is led to described server network by communication device one
News, described wired home tonsillitis care appliances is by communication device two and described server network communication.
Further, described wired home tonsillitis 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 predicts a large amount of patient in hospital pathological data by neural network model training, finds tonsillitis pathology
With tonsillitis earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, between these several the causes of disease
Logic association and variable, ultimately form the tonsillitis pathology neural network model of probability Accurate Prediction ill to tonsillitis,
The present invention is by gathering user's daily life data, and the periodicity of its data of active analysis, regularity are eventually through tonsillitis
Pathology Neural Network model predictive user suffers from tonsillitis probability, reminds user's instant hospitalizing with pre-in the way of visual effect
Anti-.
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 be to embodiment or existing
In having technology to describe, the required accompanying drawing used is briefly described, it should be apparent that, the accompanying drawing in describing below is only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to
Other accompanying drawing is obtained 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 tonsillitis Forecasting Methodology that the present invention provides, bag
Include following steps:
Step (1), acquisition hospital tonsillitis etiology and pathology data source and patient's daily monitoring data, thus set up almond
Body buring sun regular data data base；
The most daily monitoring data are 21 item data, and its 21 item data is the age, sex, heart rate, frequency of taking food, pharyngalgia feelings
Condition, drink water frequency, body weight, digest situation, food pungent zest degree, time for falling asleep, every day smoking capacity, limbs skin feelings
Condition, body temperature, 21 item data such as pursue an occupation, and the present invention sets up 21 dimensional vectors with 21 item data；
Step (2), the daily data database of tonsillitis set up according to step (1) are off-line manner to neutral net
Model is trained, to obtain the tonsillitis 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 tonsillitis pathology neural network model trained in input step (2) after doing normalized carries out tonsillitis probability
Prediction, obtains tonsillitis probability, and tonsillitis probability is sent to wired home tonsillitis care appliances by server；
After step (5), wired home tonsillitis care appliances receive the tonsillitis probability that server transmits, it is judged that flat
Whether Fructus Persicae body inflammation probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained tonsillitis, and attention device warns to carry
Wake up user, if less than 0.5, is then judged to that this user does not obtain tonsillitis；
Step (6), when user is judged to tonsillitis, user removes examination in hospital voluntarily, and inspection result is led to
Crossing wired home tonsillitis care appliances and send back server, server judges to check that result is the most correct, if checking knot
Really mistake, then explanation tonsillitis pathology Neural Network model predictive is inaccurate, if checking that result is correct, then tonsil is described
Scorching pathology Neural Network model predictive is accurate；
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, sick to tonsillitis
Reason 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
(the most tonsillitic probability), extracts 400000 records from tonsillitis daily data database table and is trained, every
Record is 21 dimensional vectors, and all data are before use first through normalized so that it is numerical value is interval in [0,1], then holds
Neural network model is trained by row following steps:
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:
Wherein: WkIt 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: WjT () is neuron；Wj(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；DjIt 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 tonsillitis care appliances of the present invention will check that result sends back the lattice of the object information of server
Formula is: { checking whether correct, blood glucose value }, and server is after receiving object information, it is judged that check that result is the most correct.
The increasable algorithm that tonsillitis pathology neural network model carries out dynamic corrections 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 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 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) from VX, select a vectorial VXiDo following judgement:
If VXiFor 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 VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe neuron work that value is maximum
For triumph neuron, update this triumph neuron main label；
If can not find main label and VXiThe identical neuron of label, then find and VXiClosest 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
{VXi, calculate the main label of triumph neuron, main label ratio riWith comentropy EiIf. 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 neuroni, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows:
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
Wherein: niRepresenting 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 Ei> 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 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 and oneself have suffered from tonsillitis, and intelligence
The attention device of energy family tonsillitis care appliances does not warn, then it represents that wired home tonsillitis care appliances judges inaccurate
Really, now performing step (6)～(7), wired home tonsillitis care appliances is sent to object information on server.
Present invention also offers the prognoses system of a kind of described tonsillitis Forecasting Methodology, including intelligent monitoring device, intelligence
Energy device data acquisition device, server and wired home tonsillitis care appliances, described intelligent monitoring device and described intelligence
Device data acquisition device is connected, and described smart machine data acquisition unit is led to described server network by communication device one
News, described wired home tonsillitis care appliances is by communication device two and described server network communication.
It is provided with attention device on the described wired home tonsillitis 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 by neural network model training predict a large amount of patient in hospital pathological data, find tonsillitis pathology with
Tonsillitis earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, patrolling between these several the causes of disease
Collect association and variable, ultimately form the tonsillitis pathology neural network model of probability Accurate Prediction ill to tonsillitis, this
Inventing by gathering user's daily life data, the periodicity of its data of active analysis, regularity are sick eventually through tonsillitis
Reason Neural Network model predictive user suffers from tonsillitis probability, reminds user's instant hospitalizing with pre-in the way of visual effect
Anti-.
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 tonsillitis morbidity also can be different.
The most conventional is the highest by neural network prediction tonsillitic method accuracy rate.The present invention is directed to each equipment user set up
Train the neural network model for this user, running after a period of time, this user is being made generation to measure nerve
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 tonsillitis care appliances
Device, for this user's dynamic corrections neural network model, when next time, similar characteristics data occurred in this user, will not miss again
Sentence.Therefore, along with the increase of the time of use, the judgement of the wired home tonsillitis care appliances of the present invention will be more and more accurate
Really.
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 tonsillitis Forecasting Methodology based on increment type neural network model, it is characterised in that comprise the steps:
Step (1), obtain hospital tonsillitis and cure the disease etiology and pathology data source and patient's daily monitoring data, thus set up almond
Body buring sun regular data data base；
Step (2), the daily data database of tonsillitis set up according to step (1) are off-line manner to neural network model
It is trained, to obtain the tonsillitis 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 tonsillitis pathology neural network model trained in input step (2) after change process carries out tonsillitis probabilistic forecasting,
Obtaining tonsillitis probability, tonsillitis probability is sent to wired home tonsillitis care appliances by server；
After step (5), wired home tonsillitis care appliances receive the tonsillitis probability that server transmits, it is judged that tonsil
Whether scorching probit is more than 0.5, if greater than 0.5, is then judged to that this user obtained tonsillitis, and attention device warns to remind use
Family, if less than 0.5, is then judged to that this user does not obtain tonsillitis；
Step (6), when user is judged to tonsillitis, user removes examination in hospital voluntarily, and inspection result is passed through intelligence
Can send back server by family's tonsillitis care appliances, server judges to check that result is the most correct, if checking that result is wrong
By mistake, then explanation tonsillitis pathology Neural Network model predictive is inaccurate, if checking that result is correct, then explanation tonsillitis is sick
Reason Neural Network model predictive 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, neural to tonsillitis pathology
Network model carries out dynamic corrections；
Step (8), repetition step (3)～(7).
A kind of tonsillitis Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature
Being, 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 tonsil
Extracting k bar record in buring sun regular data database table to be trained, every record is a n-dimensional vector, and all data are using
Front elder generation is through normalized so that it is numerical value is interval in [0,1], then performs following steps and is trained neural network model:
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: WkIt 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: 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 0.62；DjIt 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 tonsillitis Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature
Being, the form of the object information that inspection result sends back server is by wired home tonsillitis care appliances: { inspection is
No correctly, blood glucose value }, server is after receiving object information, it is judged that check result the most correct.
A kind of tonsillitis Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature
Being, the increasable algorithm that tonsillitis pathology neural network model carries out dynamic corrections is:
Vectorial for every in incremental data table V{V1,V2,…,Vn, 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 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) from VX, select a vectorial VXiDo following judgement:
If VXiFor 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 VXiFor the data of tape label, then select main label and VXiLabel is identical and riThe neuron of value maximum is as obtaining
Victory neuron, updates this triumph neuron main label；
If can not find main label and VXiThe identical neuron of label, then find and VXiClosest 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 winsi,
Calculate the main label of triumph neuron, main label ratio riWith comentropy EiIf. 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 neuroni, neuronal messages entropy EiAnd son
The average quantization error MQE of net, formula is as follows:
Wherein: WiFor the weight vector of neuron i, CiFor being mapped to the set that all input vectors of neuron i are constituted；
Wherein: niRepresenting 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 Ei> 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 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 tonsillitis Forecasting Methodology based on increment type neural network model the most according to claim 1, its feature
It is, if user includes health check-up by other means and checks oneself, learns and oneself have suffered from tonsillitis, and wired home tonsil
The attention device of scorching care appliances does not warn, then it represents that wired home tonsillitis care appliances judges inaccurate, now performs
Step (6)～(7), wired home tonsillitis care appliances is sent to object information on server.
6. one kind uses the prognoses system of tonsillitis Forecasting Methodology described in claim 1～6, it is characterised in that include intelligence
Monitoring device, smart machine data acquisition unit, server and wired home tonsillitis care appliances, described intelligent monitoring device
Being connected with described smart machine data acquisition unit, described smart machine data acquisition unit is by communication device one and described service
Device network communication, described wired home tonsillitis care appliances is by communication device two and described server network communication.
The prognoses system of tonsillitis Forecasting Methodology the most according to claim 7, it is characterised in that described wired home almond
It is provided with attention device on body inflammation care appliances.
The prognoses system of tonsillitis Forecasting Methodology the most according to claim 7, 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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