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

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.
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.
GSVMA: A Genetic-Support Vector Machine-Anova method for CAD diagnosis based on Z-Alizadeh Sani dataset
http://arxiv.org/abs/2108.08292v1