GSVMA: A Genetic Support Vector Machine ANOVA Method for CAD Diagnosis.

Javad Hassannataj Joloudari,Roohallah Alizadehsani,Edris Hassannatajjeloudari,Faezeh Azizi,Amir Mosavi,Issa Nodehi,Mohammad Ali Nematollahi

doi:10.3389/fcvm.2021.760178

Abstract

BackgroundCoronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly and have side effects. One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis.MethodsHence, this paper provides a new hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA). The analysis of variance (ANOVA) is known as the kernel function for the SVM algorithm. The proposed model is performed based on the Z-Alizadeh Sani dataset so that a genetic optimization algorithm is used to select crucial features. In addition, SVM with ANOVA, linear SVM (LSVM), and library for support vector machine (LIBSVM) with radial basis function (RBF) methods were applied to classify the dataset.ResultsAs a result, the GSVMA hybrid method performs better than other methods. This proposed method has the highest accuracy of 89.45% through a 10-fold crossvalidation technique with 31 selected features on the Z-Alizadeh Sani dataset.ConclusionWe demonstrated that SVM combined with genetic optimization algorithm could be lead to more accuracy. Therefore, our study confirms that the GSVMA method outperforms other methods so that it can facilitate CAD diagnosis.

Highlights

Cardiovascular disease (CVD) is one of the most prevalent diseases which cause a lot of deaths worldwide [1]
We have used RapidMiner Studio version 9.9 to implement the methods in the coronary artery disease (CAD) diagnosis and classification process
By comparing the performance of the methods, the ACC rates of the Support Vector Machine (SVM) with analysis of variance (ANOVA), linear SVM (LSVM), and library for support vector machine (LIBSVM) with radial basis function (RBF) were achieved 85.13, 86.11, and 84.78%, respectively, whereas the ACC rate of the genetic support vector machine with ANOVA (GSVMA) method is 89.45% based on the 10-fold crossvalidation (10-FCV) technique

Summary

Introduction

Cardiovascular disease (CVD) is one of the most prevalent diseases which cause a lot of deaths worldwide [1]. As crucial evidence for this fact, one can refer to the CVD fact sheet published by the World Health Organization (WHO), which estimated 17.9 million deaths from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to heart attack and stroke [2]. An essential type of CVDs is coronary artery disease (CAD) [3]. Coronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The challenges of CAD diagnosis using angiography are costly and have side effects. One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis

Methods

Results

Discussion

Conclusion