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

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.

Automated summary

This study proposed a hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA) for diagnosing coronary artery disease (CAD). Through testing on a dataset, it was found that this model achieved the highest accuracy and outperformed other methods. The results demonstrated that support vector machine combined with genetic optimization algorithm could improve the accuracy of CAD diagnosis.