Computational and Mathematical Methods in Medicine / 2014 / Article / Tab 7 / Research Article
An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach Table 7 The results obtained from four algorithms in the original and weighted PD dataset.
Methods Performance metrics Original feature space without SCFW method Weighted feature space with SCFW method KELM-RBF ACC (%) 95.89 ± 4.66 99.49 ± 1.15 Sensitivity (%) 96.35 ± 5.19 100 ± 0 Specificity (%) 95.72 ± 5.93 99.39 ± 1.36 AUC (%) 96.04 ± 4.06 99.69 ± 0.68 -measure0.9724 0.9966 Kappa 0.8925 0.9863 Time (s) 0.00435 0.0126 SVM ACC (%) 95.38 ± 1.15 97.95 ± 2.15 Sensitivity (%) 85.09 ± 10.45 96.67 ± 7.45 Specificity (%) 98.67 ± 2.98 98.71 ± 1.77 AUC (%) 91.88 ± 4.14 97.69 ± 3.46 -measure0.9699 0.9863 Kappa 0.8711 0.9447 Time (s) 1.24486 1.29817 KNN ACC (%) 95.38 ± 5.25 97.43 ± 3.14 Sensitivity (%) 92.73 ± 11.85 97.78 ± 4.97 Specificity (%) 96.50 ± 4.38 97.38 ± 4.10 AUC (%) 94.61 ± 6.95 97.58 ± 2.60 -measure0.9692 0.9828 Kappa 0.8765 0.9431 Time (s) 1.2847 1.3226 ELM ACC (%) 89.23 ± 6.88 96.92 ± 4.21 Sensitivity (%) 73.94 ± 13.18 95.78 ± 5.79 Specificity (%) 93.35 ± 6.27 97.19 ± 4.51 AUC (%) 83.64 ± 9.06 96.48 ± 4.36 -measure83.64 ± 9.06 0.9863 Kappa 0.7078 0.9447 Time (s) 1.1437 1.2207