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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