Optimizing heart disease diagnosis with advanced machine learning models: a comparison of predictive performance

Table 3 Individual classifier results were evaluated

S.No	Models	Accuracy	Precision	Recall	F1Score
1	Logistic Regression	0.83	0.84	0.78	0.81
2	Random Forest	0.91	0.92	0.90	0.91
3	SVM Regression	0.83	0.84	0.79	0.82
4	KNN	0.91	0.91	0.91	0.91
5	GBM	0.87	0.87	0.86	0.86
6	Neural Network	0.84	0.85	0.80	0.82
7	XGBoost	0.93	0.92	0.92	0.92
8	MANN	0.84	0.86	0.79	0.82
9	FDA	0.84	0.85	0.82	0.83
10	CIT	0.81	0.78	0.82	0.80
11	Bagged tree	0.93	0.93	0.92	0.93
12	Navi Bayes	0.85	0.87	0.80	0.83
13	MARS	0.84	0.85	0.82	0.83
14	BGLM	0.83	0.84	0.80	0.82
15	BGGLM	0.83	0.84	0.79	0.82

ISSN: 1471-2261