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Table 8 Comparison of the performance of Naïve Bayesian classifier (BC) using three different Weka options for handling continuous attributes: single normal, kernel estimation and supervised discretization using Sampling

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

  Single Normal kernel Estimation Supervised Discretization
Sensitivity 35.32% 40.90% 37.41%
Specificity 93.26% 92.37% 93.32%
Precision 52.34% 42.70% 52.20%
F-score 42.18% 41.78% 43.59%
RMSE 0.35 0.32 0.34
AUC 0.81 0.81 0.82