An artificial intelligence-based risk prediction model of myocardial infarction

BMC Bioinformatics

Table 4 Voting traversal results of Model 4 and Model 5

	Vote difference	Accuracy	F1 score	Negative precision	Positive precision	Specificity	Sensitivity
Model4_1	1	0.86	0.66	0.99	0.27	0.86	0.82
Model4_3	3	0.87	0.68	0.99	0.29	0.88	0.8
Model4_5	5	0.89	0.69	0.98	0.32	0.9	0.77
Model4_7	7	0.90	0.71	0.98	0.35	0.91	0.75
Model4_9	9	0.92	0.73	0.98	0.39	0.93	0.72
Model4_11	11	0.93	0.74	0.98	0.42	0.94	0.7
Model4_13	13	0.94	0.76	0.95	0.47	0.95	0.66
Model4_15	15	0.95	0.78	0.98	0.54	0.97	0.62
Model5_0	0	0.85	0.66	0.99	0.27	0.86	0.82
Model5_1	1	0.87	0.68	0.99	0.29	0.88	0.80
Model5_2	2	0.89	0.69	0.99	0.32	0.89	0.78
Model5_3	3	0.89	0.70	0.98	0.34	0.91	0.76
Model5_4	4	0.90	0.71	0.98	0.35	0.91	0.75
Model5_5	5	0.92	0.73	0.98	0.39	0.93	0.72
Model5_6	6	0.93	0.74	0.98	0.42	0.94	0.70
Model5_7	7	0.94	0.76	0.98	0.47	0.95	0.67
Model5_8	8	0.94	0.77	0.98	0.51	0.96	0.64
Model5_9	9	0.95	0.78	0.98	0.54	0.97	0.62

ISSN: 1471-2105