CN112052874A - Physiological data classification method and system based on generation countermeasure network - Google Patents
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Abstract
The invention discloses a physiological data classification method based on a generation countermeasure network, which comprises the following steps: acquiring relevant diagnosis data of a certain disease to be predicted; training by utilizing the diagnostic data to generate a confrontation network and generating a large number of virtual data sets; training a plurality of weak classifiers by using a virtual data set; and inputting the acquired diagnostic data into the trained weak classifier to obtain different physiological data classification results. According to the technical scheme, a large amount of virtual diabetes diagnosis data are generated on the basis of generation of the countermeasure network, a large amount of weak classifiers are trained from the virtual data, and finally the weak classifiers are integrated to obtain a more accurate disease (diabetes) integrated diagnosis result.
Description
Technical Field
The invention belongs to the technical field of physiological data processing, and particularly relates to a physiological data classification method and system based on a generation countermeasure network.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In the medical field, prediction of certain diseases requires a large number of relevant data samples, however, in many cases, it is difficult to obtain a large number of high-quality data samples, and there is a great obstacle to training of subsequent models. Accurate prediction and classification of diseases is one of the keys to disease treatment intervention, for example, diabetes, which is one of the most common chronic diseases, is predicted early and effective intervention is performed, and about 6-10% of patients do not develop diabetes every year, so efficient and accurate classification and prediction of diseases are particularly important.
Taking diabetes as an example, the existing diagnosis method mainly detects postprandial blood sugar and glycosylated hemoglobin and evaluates the postprandial blood sugar and the glycosylated hemoglobin, and has high detection precision and higher cost; on the other hand, the diagnosis can be performed by the personal experience of the doctor, but the diagnosis is misdiagnosed and missed in a long time. In recent years, more and more researchers carry out disease diagnosis according to clinical data through methods such as machine learning and statistical analysis, and generally, the methods need complete data to carry out model training, however, a large amount of manpower and material resources are needed for obtaining the clinical data, and the obtained physiological data number often has the problems of small data volume, poor data quality and the like, so that the traditional disease diagnosis mode based on data is difficult to exert good performance.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a physiological data classification method based on a generation countermeasure network, which can obtain more accurate integrated diagnosis results.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
in a first aspect, a physiological data classification method based on generation of an countermeasure network is disclosed, which includes:
acquiring relevant diagnosis data of a certain disease to be predicted;
training by utilizing the diagnostic data to generate a confrontation network and generating a large number of virtual data sets;
training a plurality of weak classifiers by using a virtual data set;
and inputting the acquired diagnostic data into the trained weak classifier to obtain different physiological data classification results.
In a second aspect, a physiological data classification system based on generation of an antagonistic network is disclosed, comprising:
a data acquisition module configured to: acquiring relevant diagnosis data of a certain disease to be predicted;
a virtual data set generation module configured to: training by utilizing the diagnostic data to generate a confrontation network and generating a large number of virtual data sets;
a classification module configured to: training a plurality of weak classifiers by using a virtual data set;
and inputting the acquired diagnostic data into the trained weak classifier to obtain different physiological data classification results.
The above one or more technical solutions have the following beneficial effects:
according to the technical scheme, a large amount of virtual disease diagnosis data are generated on the basis of generation of the countermeasure network, a large amount of weak classifiers are trained from the virtual data, and finally the weak classifiers are integrated to obtain a more accurate disease (diabetes) integrated diagnosis result.
The technical scheme of the method introduces the generation of the countermeasure network, generates a large amount of virtual data to train each sub-classifier of the integrated classifier, and the idea that the results of the sub-classifiers are similar and different in the integrated prediction is consistent with the idea that the generation of the countermeasure network generates similar and different data.
The model provided by the technical scheme disclosed by the invention needs less training data, is more suitable for the fields with less data quantity, such as diabetes prediction, and the like, and the effect of the trained integrated model is superior to that of the original stepping algorithm.
The prediction method provided by the invention has the advantages that the final training set does not participate in the integrated model training, the training result is basically the same as the test result, and the overfitting problem does not exist.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a model architecture diagram of a diabetes integrated prediction system based on generation of an antagonistic network in accordance with an embodiment of the present invention;
fig. 2 is a diagram of a model architecture for creating a countermeasure network in an embodiment of the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
The embodiment discloses a physiological data classification method based on generation of an antagonistic network, which is described by taking diabetes diagnosis as an example, and is shown in the accompanying figure 1, and comprises the following steps:
s1, acquiring the diabetes diagnosis data, and performing data preprocessing including missing data processing, data normalization and the like to obtain a diabetes diagnosis data set.
S2 separates the diagnostic data set into a test set R and a training set S.
S3 trains the GAN model with the training set S and generates a large number of virtual data sets.
S4 denormalizes the virtual data set to obtain a normalized virtual data set V.
S5 trains ten weak classifier models in the second layer model using the virtual data set V.
S6 training a third-layer model by taking the result of the weak classifier as input, wherein the third-layer model generally adopts a logistic regression model (LR) which mainly gives a weight to each classifier of the second-layer model, and the final result is obtained after the classification results of all the classifiers of the second layer are weighted and averaged.
The embodiment of the disclosure is mainly directed to the problem of classification based on a small amount of data, and when the amount of data is small, the classification effect of the method is more advantageous.
In step S1, diabetes diagnosis data including age, sex, number of pregnancies, blood glucose level, blood pressure, body mass index, genetic index, whether or not diabetes is present, and the like are acquired, data with missing values greater than 50% are deleted, and the remaining data are subjected to missing value processing by a method such as multiple interpolation.
It should be noted here that clinical data of each patient includes tens of items, but different hospitals and doctors record different items, and if more than 50% of patients in the data do not detect the item, the item is deleted.
Dividing the data set into a test set and a training set;
dividing a data set into a test set R and a training set S, normalizing the data of the test set, and training the model and the use method of the test set by using the training set;
the further formula for normalizing the data is as follows:
wherein a isi，jAs raw data, Ai，jTo normalize the data, max (a)i，j) And min (a)i，j) The maximum and minimum values of the raw data. Where sex-like boolean quantities are converted to 0 and 1.
In step S2, 70% of the randomly selected data is divided into training set S, and the remaining 30% is test set R.
Referring to fig. 2, in step S3, a generation countermeasure network (GAN) is composed of two parts, a generation model G and a discrimination model D. G is input by normally distributed random noise, and output is a virtual sample G (z), G (z) obeys the real diabetes diagnosis data distribution PdataTo confuse D. And D, inputting a training set S and a virtual sample G (z) in the real data, outputting a judgment result scale belonging to (0, 1), judging that the input data is the real data when the scale is more than 0.5, and judging that the data is the virtual data when the scale is less than 0.5, wherein the judgment result scale belongs to the virtual sample G (z) and is used for judging the quality of the data generated by G. And when the judgment results of the training set S and the virtual sample G (z) are both 0.5, finishing the training. G and D can be nonlinear mapping functions, the generator adopts a fully-connected neural network, and the discriminator adopts a long-term and short-term memory network.
Generating a countermeasure network includes two models, one for generation and one for discrimination, but they are usually split into generators and discriminators.
First, the arbiter is optimized given the generator. The discriminator is a binary model, the training discriminator is a process for realizing the minimum cross entropy, and the loss function of the GAN model has the following formula:
e (-) is the calculation of the expected value, x is sampled in the real data distribution Pdata(x) For diagnostic data, z is sampled in a prior distribution Pz(z), z is a random number. The generator learns the distribution of the data x from a priori noise distribution Pz(z.) A mapping space G (z; theta) is constructedg) The corresponding discriminator mapping function is D (x; thetad) And outputting a scalar to represent the probability that x is real data.
Wherein the content of the first and second substances,
wherein x represents a real sample, and D (x) represents that x is judged to be the real sample by the discrimination networkProbability;
in the above description, z represents noise of an input generation sample, g (z) represents a sample generated from the noise z in the generation network, and D (g (z)) represents a probability that the generation sample is judged to be a true sample after passing through the discrimination network. The purpose of generating the network is to make the generated sample closer to the real sample better, i.e. the closer to D (G (z)) is to 1, the better, and then V (D, G) will become smaller; the purpose of discriminating the network is to let D (x) approach 1 and D (G (z)) approach 0.
Finally, by generating a countermeasure network, a large number of virtual data sets V are finally generated.
In step S4, the data is inverse normalized to obtain a data set formula as follows:
wherein, ai，jAs raw data, Ai，jTo normalize the data, max (a)i，j) And min (a)i，j) Are the maximum and minimum values of the raw data,
for a virtual data set V ═ V1，V2...VnThe virtual dataset includes clinical test features and diabetic condition. n may be increased infinitely as G generates dummy data, which is 2000 copies of G, i.e., n is 2000.
The virtual data set V is randomly divided into ten parts, M ═ M1，M2...M10e.V, where each subdata set contains 200 pieces of data.
In step S5, ten simple classification models are trained respectively through ten subdata sets, and the classifiers are Decision Trees (DT), Random Forests (RF), extreme random trees (ET), adaboost (adb), Support Vector Machines (SVM), multi-perceptron (MLP), Naive Bayes (NBC), Gaussian Naive Bayes (GNB), Logistic Regression (LR), Neural Networks (NN), and the like.
In step S6, the training sets S are respectively put into the classification models trained in step S5 to obtain ten different classification results, and the results are used as input to train the third-level model Logistic Regression (LR) to finally complete all model training.
After the model is trained, the test set is normalized in the same way, and then the classification result is obtained through the invented model.
The technical scheme of the disclosure trains and generates a countermeasure network (GAN) by using a training set S, so that a generator G and a discriminator D in the GAN reach dynamic balance, namely the generator G can generate a false-true virtual data set V, V ═ V { (V)1，V2…Vn}；
The system is divided into three layers of models, a large amount of virtual diabetes diagnosis data are generated on the basis of generation of the countermeasure network, a large amount of weak classifiers are trained from the virtual data, and finally the weak classifiers are integrated to obtain a more accurate diabetes integrated diagnosis result.
Based on the same inventive concept, the embodiment discloses a physiological data classification system based on generation of a countermeasure network, which comprises:
a data acquisition module configured to: acquiring relevant diagnosis data of a certain disease to be predicted;
a virtual data set generation module configured to: training by utilizing the diagnostic data to generate a confrontation network and generating a large number of virtual data sets;
a classification module configured to: training a plurality of weak classifiers by using a virtual data set;
and inputting the acquired diagnostic data into the trained weak classifier to obtain different physiological data classification results.
The present embodiment is directed to a computing device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to the above embodiment.
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, performs the steps of the method of the above-described embodiment example.
The steps involved in the apparatus of the above embodiment correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A physiological data classification method based on a generation countermeasure network is characterized by comprising the following steps:
acquiring relevant diagnosis data of a certain disease to be predicted;
training by utilizing the diagnostic data to generate a confrontation network and generating a large number of virtual data sets;
training a plurality of weak classifiers by using a virtual data set;
and inputting the acquired diagnostic data into the trained weak classifier to obtain different physiological data classification results.
2. The physiological data classification method based on generation of confrontation network as claimed in claim 1, characterized in that the acquired diagnostic data is normalized and divided into a test set and a training set.
3. The physiological data classification method based on the generation countermeasure network as claimed in claim 1, wherein the generation countermeasure network comprises a generation model and a discrimination model;
for random noise with the input of the generation model being in normal distribution, the output is a virtual sample;
the input of the discrimination model is a training set and a virtual sample in real data, and the output is a discrimination result.
4. The physiological data classification method based on the generative confrontation network as claimed in claim 3, wherein the training is finished when the judgment result of the discriminant model on the training set and the virtual sample satisfies the set condition.
5. The physiological data classification method based on the generative confrontation network as claimed in claim 3, wherein the generative model adopts a fully connected neural network, and the discriminant model adopts a long-short term memory network.
6. The physiological data classification method based on the generative countermeasure network as claimed in claim 3, wherein the number of the virtual data set divided randomly is the same as the number of the weak classifiers to be trained.
7. A method for establishing a disease diagnosis model is characterized by comprising the following steps:
obtaining a classification result by using the physiological data classification method based on generation of the countermeasure network according to any one of claims 1 to 6;
and inputting the classification result into a logistic regression model to obtain a trained diagnosis module.
8. A physiological data classification system based on a generative confrontation network, comprising:
a data acquisition module configured to: acquiring relevant diagnosis data of a certain disease to be predicted;
a virtual data set generation module configured to: training by utilizing the diagnostic data to generate a confrontation network and generating a large number of virtual data sets;
a classification module configured to: training a plurality of weak classifiers by using a virtual data set;
and inputting the acquired diagnostic data into the trained weak classifier to obtain different physiological data classification results.
9. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any of claims 1 to 6 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of the preceding claims 1-6.
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| CN113657623A (en) * | 2021-07-13 | 2021-11-16 | 国网河北省电力有限公司电力科学研究院 | Power equipment state diagnosis effect determination method and device, terminal and storage medium |
| CN113782182A (en) * | 2021-07-29 | 2021-12-10 | 北京理工大学 | Stress fracture prediction method based on electromyographic data |
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