Feature Selection (FS)

Feature Selection (FS) has become the motivation of much research on decision support systems areas for which datasets with large number of features are analyzed. This paper presents a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapper Bayes Naïve (BN). Initially, thirteen attributes were involved in predicting CAD. In GA–BN algorithm, GA produces in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final result set of attribute holds the most pertinent feature model that increases the accuracy. The accuracy results showed that the algorithm produces 85.50% classification accuracy in the diagnosis of CAD. Therefore, the strength of the Algorithm is then compared with other machine learning algorithms such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Then, the GA wrapper BN Algorithm is similarly compared with other FS algorithms. The Obtained results have shown very favorable outcomes for the diagnosis of CAD.

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Feature Selection (FS) has become the motivation of much research on decision support systems areas for which datasets with large number of features are analyzed. This paper presents a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapper Bayes Naïve (BN). Initially, thirteen attributes were involved in predicting CAD. In GA–BN algorithm, GA produces in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final result set of attribute holds the most pertinent feature model that increases the accuracy. The accuracy results showed that the algorithm produces 85.50% classification accuracy in the diagnosis of CAD. Therefore, the strength of the Algorithm is then compared with other machine learning algorithms such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Then, the GA wrapper BN Algorithm is similarly compared with other FS algorithms. The Obtained results have shown very favorable outcomes for the diagnosis of CAD.