Table 8 Performance metrics of various machine learning algorithms Evaluated.

Algorithm	Accuracy	Precision	Recall	F1-Score	Area Under Curve	Hamming Loss	Jaccard Score	Log Loss	Mathews Correlation Coefficient
Grid Search
Random Forest	0.8	0.8	0.80	0.79	0.84	0.205	0.672	7.389	0.5959
KNN	0.74	0.76	0.75	0.74	0.81	0.255	0.617	9,191	0.501
Decision Tree	0.66	0.68	0.66	0.65	0.71	0.345	0.5369	12.435	0.333
catBoost	0.78	0.78	0.78	0.77	0.84	0.225	0.643	8.109	0.554
Logistic Regression	0.74	0.75	0.75	0.74	0.84	0.255	0.605	9.191	0.494
Ensemble Stack	0.76	0.76	0.76	0.76	0.84	0.24	0.607	8.65	0.519
Randomized Search
Random Forest	0.79	0.81	0.79	0.79	0.86	0.21	0.6865	7.5691	0.5945
KNN	0.72	0.73	0.72	0.72	0.8	0.2775	0.6007	10.0021	0.4519
Decision Tree	0.66	0.71	0.66	0.64	0.72	0.3375	0.5781	12.1647	0.3689
catBoost	0.78	0.79	0.78	0.77	0.83	0.225	0.6703	8.1098	0.5663
Logistic Regression	079	0.8	0.79	0.79	0.83	0.2125	0.6755	7.6592	0.5818
Ensemble Stack	0.76	0.77	0.76	0.76	0.83	0.24	0.6404	8.6504	0.5255
Bayesian Optimization Search
Random Forest	0.77	0.79	0.77	0.77	0.84	0.2275	0.6654	8.1999	0.559
KNN	0.72	0.74	0.72	0.71	0.8	0.2825	0.6076	10.1823	0.4513
Decision Tree	0.74	0.75	0.74	0.73	0.8	0.2625	0.6196	0.8643	0.4836
catBoost	0.74	0.76	0.74	0.73	0.83	0.2625	0.66315	9.4614	0.4947
Logistic Regression	0.75	0.76	0.75	0.75	0.81	0.25	0.6282	9.0109	0.5052
Ensemble Stack	0.79	0.79	0.79	0.79	0.85	0.2125	0.6718	7.6592	0.5793
Neural Network
ANN	0.74	0.75	0.74	0.74	-	-	-	-	-

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