Mitigating urinary incontinence condition using machine learning

Table 5 Testing and validation results for different models

Model	Feature selection	Precision	Recall	F1-score	Accuracy
NB	Lasso_SFM	0.68	0.52	0.59	0.52
	DT_SFM	0.63	0.64	0.63	0.64
	RF_SFM	0.63	0.64	0.63	0.64
	chi_SKB	0.67	0.63	0.64	0.63
	DT_RFE	0.67	0.65	0.65	0.65
	RF_RFE	0.66	0.65	0.65	0.65
	Lasso_RFE	0.66	0.61	0.63	0.61
	X_all	0.69	0.63	0.65	0.63
SVM	Lasso_SFM	0.68	0.55	0.60	0.55
	DT_SFM	0.65	0.40	0.48	0.40
	RF_SFM	0.67	0.54	0.59	0.54
	chi_SKB	0.66	0.45	0.52	0.45
	DT_RFE	0.67	0.56	0.61	0.56
	RF_RFE	0.66	0.51	0.57	0.51
	Lasso_RFE	0.67	0.56	0.61	0.56
	X_all	0.66	0.54	0.58	0.54
KNN	Lasso_SFM	0.76	0.29	0.38	0.29
	DT_SFM	0.65	0.54	0.58	0.54
	RF_SFM	0.65	0.53	0.58	0.53
	chi_SKB	0.65	0.54	0.58	0.54
	DT_RFE	0.67	0.57	0.61	0.57
	RF_RFE	0.66	0.55	0.59	0.55
	Lasso_RFE	0.60	0.63	0.61	0.63
	X_all	0.65	0.51	0.56	0.51
NN	Lasso_SFM	0.68	0.51	0.57	0.51
	DT_SFM	0.63	0.64	0.63	0.64
	RF_SFM	0.63	0.64	0.63	0.64
	chi_SKB	0.63	0.64	0.63	0.64
	DT_RFE	0.63	0.64	0.63	0.64
	RF_RFE	0.63	0.64	0.63	0.64
	Lasso_RFE	0.65	0.60	0.62	0.60
	X_all	0.63	0.64	0.63	0.64
XGB	Lasso_SFM	0.71	0.67	0.68	0.67
	DT_SFM	0.68	0.65	0.66	0.65
	RF_SFM	0.68	0.67	0.67	0.67
	chi_SKB	0.70	0.70	0.70	0.70
	DT_RFE	0.67	0.71	0.69	0.71
	RF_RFE	0.69	0.71	0.70	0.71
	Lasso_RFE	0.66	0.65	0.65	0.65
	X_all	0.70	0.70	0.70	0.70

ISSN: 1472-6947