Development of Hepatitis Disease Detection System by Exploiting Sparsity in Linear Support Vector Machine to Improve Strength of AdaBoost Ensemble Model

Table 3

Performance of the proposed sparse SVM and AdaBoost-based learning system at optimal hyperparameters of the two models on hepatitis disease data.

					Sens. (%)	Spec. (%)
1	0.01	1	80.85	86.11	22.22	94.73	0.239
2	0.015	1	80.85	86.11	22.22	94.73	0.239
3	0.02	1	80.85	86.11	22.22	94.73	0.239
4	0.04	1	80.85	86.11	22.22	94.73	0.239
5	0.06	1	80.85	86.11	22.22	94.73	0.239
6	0.065	1	80.85	86.11	22.22	94.73	0.239
7	0.07	1	80.85	86.11	22.22	94.73	0.239
8	0.085	1	80.85	86.11	22.22	94.73	0.239
9	0.088	1	80.85	86.11	22.22	94.73	0.239
10	0.09	1	80.85	86.11	22.22	94.73	0.239
11	0.1	8	82.97	90.74	22.22	97.36	0.315
16	0.3	75	89.36	100.0	44.44	100	0.626
17	0.9	36	87.23	100.0	33.33	100.0	0.536
18	3	3	82.97	85.18	11.11	100.0	0.302
19	—	3	82.97	85.18	11.11	100.0	0.302

Bold values indicate optimal performance.