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Coronary heart disease (CAD) is one of the crucial reasons for cardiovascularmortality in middle-aged people worldwide. The most typical tool is angiographyfor diagnosing CAD. The challenges of CAD diagnosis using angiography arecostly and have side effects. One of the alternative solutions is the use ofmachine learning-based patterns for CAD diagnosis. Hence, this paper provides anew hybrid machine learning model called Genetic Support Vector Machine andAnalysis of Variance (GSVMA). The ANOVA is known as the kernel function forSVM. The proposed model is performed based on the Z-Alizadeh Sani dataset. Agenetic optimization algorithm is used to select crucial features. In addition,SVM with Anova, Linear SVM, and LibSVM with radial basis function methods wereapplied to classify the dataset. As a result, the GSVMA hybrid method performsbetter than other methods. This proposed method has the highest accuracy of89.45% through a 10-fold cross-validation technique with 35 selected featureson the Z-Alizadeh Sani dataset. Therefore, the genetic optimization algorithmis very effective for improving accuracy. The computer-aided GSVMA method canbe helped clinicians with CAD diagnosis.

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