Table 3.

Quantitative comparison among deep learning, various handcrafted and ensemble feature extraction methods (Classifier wise precision (in %))

Features	Gaussian Naïve Bayes	Decision Tree	Random Forest	XGB Classifier
F1	53.47	53.67	57.51	61.62
F1 + F2	66.13	67.86	68.96	71.40
F1 + F3	63.15	62.91	64.85	69.63
F1 + F4	70.68	72.04	73.35	77.09
F1 + F5	68.75	70.69	72.28	75.59
F1 + F2 + F3	74.46	76.64	76.63	79.70
F1 + F2 + F4	80.37	80.97	81.78	83.58
F1 + F2 + F5	77.05	77.56	78.18	82.22
F1 + F3 + F4	77.56	79.70	79.93	83.63
F1 + F3 + F5	73.85	76.54	76.80	82.57
F1 + F4 + F5	80.92	83.58	84.17	83.61
F1 + F2 + F3 + F4	86.16	88.63	90.13	88.99
F1 + F2 + F3 + F5	81.89	83.36	83.24	87.96
F1 + F2 + F4 + F5	89.42	89.50	90.38	90.47
F1 + F3 + F4 + F5	85.17	87.86	89.16	88.77
F1 + F2 + F3 + F4 + F5	91.90	92.66	93.70	93.10

Bold face of text depicting the maximum accuracy achieved in each table