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Computational and Mathematical Methods in Medicine
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Computational and Mathematical Methods in Medicine
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2014
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Article
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Tab 5
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Research Article
An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach
Table 5
Results of SCFW-KELM with different types of kernel functions in the PD dataset.
Kernel type
Performance metrics
Mean
SD
Max
Min
RBF_kernel
ACC (%)
99.49
1.15
100
97.44
Sensitivity (%)
100
0
100
100
Specificity (%)
99.39
1.36
100
96.97
AUC (%)
99.69
0.68
100
98.48
-measure
0.9966
Kappa
0.9863
Wav_kernel
ACC (%)
96.92
2.15
100
94.87
Sensitivity (%)
98.46
3.44
100
92.31
Specificity (%)
96.54
2.39
100
93.33
AUC (%)
97.50
2.18
100
94.23
-measure
0.9793
Kappa
0.9194
Lin_kernel
ACC (%)
96.92
2.15
100
94.87
Sensitivity (%)
90.43
8.85
100
81.82
Specificity (%)
99.29
1.60
100
96.43
AUC (%)
94.86
3.99
100
90.91
-measure
0.9798
Kappa
0.9147
Poly_kernel
ACC (%)
97.43
2.56
100
94.87
Sensitivity (%)
96.67
7.45
100
83.33
Specificity (%)
97.37
3.61
100
93.10
AUC (%)
97.02
3.42
100
91.67
-measure
0.9828
Kappa
0.9323