[Retracted] Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning

Table 6

ML result compression.

ML algorithms	Confusion matrix	Accuracy (%)	Precision		Recall		f1-score		Support
			0	1	0	1	0	1	0	1
Logistic regression	[[22 6]; [2 31]]	86.88	0.92	0.84	0.79	0.94	0.86	0.89	28	33
Naïve Bayes	[[22 6]; [4 29]]	83.60	0.85	0.83	0.79	0.88	0.81	0.85	28	33
Extreme gradient boost	[[25 3]; [0 33]]	95.08	1.00	0.92	0.89	1.00	0.94	0.96	28	33
K-neighbors classifier	[[23 5]; [1 32]]	90.16	0.96	0.86	0.82	0.97	0.88	0.91	28	33
Decision tree classifier	[[22 6]; [8 25]]	77.049	0.73	0.81	0.79	0.76	0.76	0.78	28	33
Random forest	[[28 0]; [2 31]]	96.72	0.93	1.00	1.00	0.94	0.97	0.97	28	33