Table 5.

Results based on features selection by the LASSO technique.

Features	Algorithm	Accuracy	Sensitivity	Specificity
5	Logistic	0.6962	0.7634	0.6082
	k-NN	0.6873	0.6785	0.7014
	Tree	0.6683	0.703	0.6246
	R-forest	0.718	0.7201	0.7163
	SVM	0.716	0.7255	0.7044
	NN	0.7179	0.7277	0.5918
10	Logistic	0.705	0.7593	0.6318
	k-NN	0.666	0.7	0.6233
	Tree	0.6516	0.6865	0.6078
	R-forest	0.7101	0.7116	0.7124
	SVM	0.6997	0.7756	0.5954
	NN	0.7106	0.709	0.588
15	Logistic	0.6899	0.7414	0.6223
	k-NN	0.7005	0.72	0.6764
	Tree	0.649	0.704	0.5782
	R-forest	0.7153	0.7005	0.7405
	SVM	0.7029	0.7906	0.5821
	NN	0.7119	0.6934	0.5872
20	Logistic	0.7177	0.7481	0.6778
	k-NN	0.6933	0.6897	0.7021
	Tree	0.6481	0.6991	0.5772
	R-forest	0.7308	0.7061	0.7699
	SVM	0.7047	0.7657	0.6212
	NN	0.7248	0.7009	0.5906
25	Logistic	0.701	0.7358	0.6552
	k-NN	0.6997	0.729	0.6608
	Tree	0.6458	0.6957	0.5788
	R-forest	0.7373	0.7325	0.7474
	SVM	0.7228	0.7839	0.639
	NN	0.7032	0.6901	0.5888
30	Logistic	0.6906	0.7284	0.6408
	k-NN	0.6989	0.7383	0.6483
	Tree	0.6425	0.6982	0.5682
	R-forest	0.7384	0.742	0.7376
	SVM	0.7178	0.7866	0.625
	NN	0.6946	0.6836	0.5857