Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer

Table 3

Proposed regularizer versus existing literature’s results using 10-fold CV.

Reference	Method	Accuracy	Sen.	Spe.	F-score
[37]	BSWFM	87.4%	82.5%	91.3%	None
[38]	TWIST algorithm	84.14	74.23	78.87	None
[39]	ICA + SVM	83.75	80.67	79.28	None
[40]	GA-LDA + hybrid ensemble	93.65	96.00	89.25	None
	RSD-ANN	96.30%	95.24%	93.75%	94.57%

Best results are highlighted in bold.