Table 4.

Ranked Molecular Descriptor-Based Prediction Scores for Each Machine Learning Algorithm by Metrics (Average over Three Datasets)^a

algorithms	AUC	F1_score	ACC	Cohen’s κ	MCC	precision	recall	mean	rank
SVM	0.926	0.573	0.900	0.504	0.540	0.495	0.802	0.677	2
MLP_1	0.902	0.560	0.902	0.503	0.542	0.476	0.813	0.671	3
MLP_2	0.843	0.517	0.856	0.390	0.407	0.466	0.639	0.588	11
MLP_3	0.858	0.549	0.876	0.448	0.468	0.534	0.616	0.621	9
MLP_4	0.901	0.566	0.887	0.454	0.480	0.472	0.735	0.642	7
MLP_5	0.912	0.570	0.906	0.499	0.522	0.494	0.738	0.663	6
RF	0.911	0.582	0.907	0.503	0.521	0.503	0.728	0.665	5
ABDT	0.940	0.595	0.920	0.556	0.602	0.504	0.875	0.713	1
DT	0.816	0.562	0.901	0.478	0.492	0.497	0.682	0.632	8
NB	0.925	0.561	0.878	0.470	0.526	0.451	0.887	0.671	3
logistic	0.877	0.505	0.842	0.415	0.464	0.423	0.821	0.621	9

^aEach bold entry shows the highest metric value among the machine learning models using different algorithms.