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Table 16 Comparison of the performance of the different classification models without using the SMOTE sampling method

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

  DT SVM ANN BC BN KNN RF
Sensitivity 54.43% 39.78% 52.65% 37.71% 57.09% 46.78% 59.09%
Specificity 90.51% 87.65% 91.37% 93.23% 90.71% 89.31% 90.21%
Precision 22.80% 18.03% 31.39% 51.32% 24.57% 11.12% 19.52%
F-score 32.14% 24.81% 39.33% 43.47% 34.35% 17.97% 29.35%
RMSE 0.3 0.47 0.29 0.34 0.34 0.3 0.29
AUC 0.73 0.59 0.80 0.82 0.83 0.74 0.82
  1. The models are: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). The results of this experiment show that BN achieves the highest AUC (0.83). The BC model achieves the highest precision (51.32%) and the highest specificity (93.32%)