Table 4 Evaluation Metrics for Heart Disease Prediction Models.

Model	TP	FP	FN	TN	Accuracy	Recall	Specificity	F1 Score	ROC–AUC
Decision Tree Regression	32	8	8	20	0.7647	0.8000	0.7143	0.7619	0.7624
K-Nearest Neighbor	32	8	7	21	0.7794	0.8205	0.7241	0.7742	0.8896
Support Vector Machine	32	8	6	22	0.7941	0.8421	0.7333	0.7843	0.9049
Kernel SVM	32	8	7	21	0.7794	0.8205	0.7241	0.7742	0.9048
Logistic Regression	32	8	6	22	0.7941	0.8421	0.7333	0.7843	0.9019
Naïve Bayes	32	8	7	21	0.7794	0.8205	0.7241	0.7742	0.9062
Random Forest Regression	37	8	9	19	0.8235	0.8043	0.7037	0.7989	0.9167
Random Forest with K-Means Analysis	39	1	5	23	0.9100	0.8864	0.9583	0.8977	0.9155

Note: TP=True Positive, FP=False Positive, FN=False Negative, TN=True Negative, ROC=Receiver Operating Characteristic, AUC=Area Under the (ROC) Curve.

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