A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Table 10

Excellent performance metrics results and best classifiers with feature selection algorithms for n = 6 with 10-fold CV.

	Best performances evaluation metrics and best classifiers
FS	The best accuracy (%) and classifier	The best specificity (%) and best classifier	The best sensitivity (%) and classifier	The best MCC and classifiers	The best AUC and classifiers	The best processing time(s) and classifiers
Relief	89 logistic regression with C = 100	98 logistic regression with C = 100	100 ANN (MLP) with 16	89 logistic regression with C = 100	88 logistic regression	14.134 SVM ( RBF) with C = 100, G = 0.0001
mRMR	84 Naive Bayes	100 SVM (linear) with C = 100, = 0.0001	98 ANN (MLP) with 20	83 Naive Bayes	84 Naive Bayes	1.121 random forest
LASSO	88 SVM( RBF) with C = 100, = 0.001	97 logistic regression with C = 100	78 Naive Bayes	88 SVM (RBF) with C = 100, = 0.001	89 SVM (RBF) with C = 100, = 0.001	0.005 SVM( linear) C = 100, = 0.001