GSVMA: A Genetic-Support Vector Machine-Anova method for CAD diagnosis based on Z-Alizadeh Sani dataset

TL;DR

This paper introduces GSVMA, a hybrid machine learning model combining genetic algorithms and SVM with ANOVA kernel, achieving high accuracy in CAD diagnosis using the Z-Alizadeh Sani dataset.

Contribution

The paper presents a novel hybrid GSVMA model that enhances CAD diagnosis accuracy by combining genetic feature selection with SVM-ANOVA classification.

Findings

GSVMA outperforms other classification methods.

Achieved 89.45% accuracy with 35 features.

Genetic optimization improves model performance.

Abstract

Coronary heart 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. Hence, this paper provides a new hybrid machine learning model called Genetic Support Vector Machine and Analysis of Variance (GSVMA). The ANOVA is known as the kernel function for SVM. The proposed model is performed based on the Z-Alizadeh Sani dataset. A genetic optimization algorithm is used to select crucial features. In addition, SVM with Anova, Linear SVM, and LibSVM with radial basis function methods were applied to classify the dataset. As 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 cross-validation technique with 35 selected features on the Z-Alizadeh Sani dataset. Therefore, the genetic optimization algorithm is very effective for improving accuracy. The computer-aided GSVMA method can be helped clinicians with CAD diagnosis.