Journal of Ovarian Research

Table 3 Performance of 7 machine learning models

From: A metabolic fingerprint of ovarian cancer: a novel diagnostic strategy employing plasma EV-based metabolomics and machine learning algorithms

	Algorithms
	RF	ANN	NB	KNN	SVM	DT	LR
OC vs CON
AUC	0.91	0.90	0.90	0.87	0.88	0.80	0.61
Sensitivity	0.92	0.83	0.67	0.75	0.67	0.67	0.58
Specificity	0.78	0.78	0.89	0.78	0.78	0.78	0.56
PPV	0.85	0.83	0.89	0.82	0.80	0.80	0.64
NPV	0.88	0.78	0.67	0.70	0.64	0.64	0.50
Precision	0.85	0.83	0.89	0.82	0.80	0.80	0.64
F1	0.88	0.83	0.76	0.78	0.73	0.73	0.61
OC vs BE
AUC	0.86	0.80	0.83	0.86	0.94	0.69	0.73
Sensitivity	0.80	0.60	0.80	1.00	0.80	0.80	0.80
Specificity	0.86	0.86	0.71	0.86	0.86	0.57	0.71
PPV	0.80	0.75	0.67	0.83	0.80	0.57	0.67
NPV	0.86	0.75	0.83	1.00	0.86	0.80	0.83
Precision	0.80	0.75	0.67	0.83	0.80	0.57	0.67
F1	0.80	0.67	0.73	0.91	0.80	0.67	0.73

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ISSN: 1757-2215

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