Intelligent Diagnosis Method for New Diseases Based on Fuzzy SVM Incremental Learning.

Shi Song-Men

doi:10.1155/2022/7631271

Abstract

The diagnosis of new diseases is a challenging problem. In the early stage of the emergence of new diseases, there are few case samples; this may lead to the low accuracy of intelligent diagnosis. Because of the advantages of support vector machine (SVM) in dealing with small sample problems, it is selected for the intelligent diagnosis method. The standard SVM diagnosis model updating needs to retrain all samples. It costs huge storage and calculation costs and is difficult to adapt to the changing reality. In order to solve this problem, this paper proposes a new disease diagnosis method based on Fuzzy SVM incremental learning. According to SVM theory, the support vector set and boundary sample set related to the SVM diagnosis model are extracted. Only these sample sets are considered in incremental learning to ensure the accuracy and reduce the cost of calculation and storage. To reduce the impact of noise points caused by the reduction of training samples, FSVM is used to update the diagnosis model, and the generalization is improved. The simulation results on the banana dataset show that the proposed method can improve the classification accuracy from 86.4% to 90.4%. Finally, the method is applied in COVID-19's diagnostic. The diagnostic accuracy reaches 98.2% as the traditional SVM only gets 84%. With the increase of the number of case samples, the model is updated. When the training samples increase to 400, the number of samples participating in training is only 77; the amount of calculation of the updated model is small.

Highlights

The acceleration of the pace of modern life and the aggravation of the pollution of air, water, and other living resources cause the increase of incidence disease rate [1]
When the incremental samples accumulate to a certain number, the model update program is triggered; in the model updating stage, the support vector set (SV set) and boundary sample set are extracted from the historical sample database according to the diagnosis results of historical samples, and they are added together with the boundary samples and boundary samples in the new sample set as a new training sample set, and a new diagnosis model is obtained by giving different fuzzy membership degrees to different samples for fuzzy support vector machine (FSVM) training
The diagnosis of new diseases is a challenging problem in intelligent diagnosis and treatment with machine learning

Summary

Introduction

The acceleration of the pace of modern life and the aggravation of the pollution of air, water, and other living resources cause the increase of incidence disease rate [1]. Narin et al propose three different deep learning models based on ResNet, Inception V3, and Inception-ResNet v2 [23] to detect COVID-19 from X-ray images All these studies have achieved high diagnostic accuracy, but they are based on large sample conditions. Every time the diagnostic model of standard SVM is updated, all samples need to be retrained, which costs a lot of storage and calculation To solve this problem, many scholars have proposed some SVM incremental learning methods. In the incremental learning method we intend to adopt, most of the samples will be omitted in the sample updating process In this case, if the traditional SVM training diagnosis model is still used, once some noise points or outliers with large deviation appear in the new samples, it may lead to a large deviation of the classification hyperplane, resulting in the possibility of a significant decline in the diagnosis effect.

SVM Incremental Learning Method

Algorithm Verification

Application Analysis of Intelligent Diagnosis

Findings

Conclusion