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Table 5 Comparison of the performance of Support Vector Machine (SVM) classifier without sampling using polynomial, normalized polynomial and puk kernels using complexity parameters 0.1, 10 and 30

From: Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project

 

Polynomial

Normalized Polynomial

Puk

C = 0.1

C = 10

C = 30

C = 0.1

C = 10

C = 30

C = 0.1

C = 10

C = 30

Sensitivity

0%

0%

0%

0%

0%

56.59%

0%

37.90%

39.78%

Specificity

88.30%

88.30%

88.30%

88.30%

88.30%

88.83%

88.30%

90.22%

87.65%

Precision

0%

0%

0%

0%

0.00%

5.55%

0%

22.11%

18.03%

F-score

0%

0%

0%

0%

0.00%

10.11%

0%

27.92%

24.81%

RMSE

0.34

0.34

0.34

0.34

0.34

0.34

0.34

0.37

0.47

AUC

0.50

0.50

0.50

0.5

0.5

0.52

0.5

0.58

0.59