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. 2022 Apr 6;22:664. doi: 10.1186/s12889-022-13131-x

Table 2.

The models’ performance with 95% confidence interval according to the number of features used

F1-score Accuracy Sensitivity Specificity AUC
Original SMOTE Original SMOTE Original SMOTE Original SMOTE Original SMOTE
4 Features (Demographic and anthropometric Features)
 Decision Tree 0.711 (0.66–0.76) 0.758 (0.71–0.80) 0.711 (0.66–0.76) 0.758 (0.71–0.80) 0.573 (0.52–0.63) 0.758 (0.71–0.80) 0.782 (0.74–0.83) 0.758 (0.71–0.80) 0.677 (0.63–0.73) 0.758 (0.71–0.80)
 Gaussian NB 0.789 (0.75–0.83) 0.780 (0.74–0.82) 0.790 (0.75–0.83) 0.780 (0.74–0.82) 0.684 (0.63–0.73) 0.790 (0.75–0.83) 0.844 (0.80–0.88) 0.769 (0.72–0.81) 0.764 (0.72–0.81) 0.780 (0.74–0.82)
 KNN 0.774 (0.73–0.82) 0.783 (0.74–0.83) 0.777 (0.73–0.82) 0.783 (0.74–0.83) 0.619 (0.57–0.67) 0.826 (0.79–0.87) 0.859 (0.82–0.90) 0.740 (0.69–0.79) 0.739 (0.69–0.79) 0.783 (0.74–0.83)
 XGBoost 0.771 (0.73–0.82) 0.802 (0.76–0.84) 0.773 (0.73–0.82) 0.802 (0.76–0.85) 0.626 (0.57–0.68) 0.812 (0.77–0.85) 0.848 (0.81–0.89) 0.792 (0.75–0.84) 0.737 (0.69–0.78) 0.802 (0.76–0.85)
 RF 0.772 (0.73–0.82) 0.813 (0.77–0.86) 0.774 (0.73–0.82) 0.814 (0.77–0.86) 0.628 (0.58–0.68) 0.832 (0.79–0.87) 0.850 (0.81–0.89) 0.795 (0.75–0.84) 0.739 (0.69–0.79) 0.814 (0.77–0.86)
 Logistic R 0.777 (0.73–0.82) 0.783 (0.74–0.83) 0.787 (0.74–0.83) 0.784 (0.74–0.83) 0.558 (0.50–0.61) 0.799 (0.76–0.84) 0.904 (0.87–0.94) 0.768 (0.72–0.81) 0.731 (0.68–0.78) 0.784 (0.74–0.83)
 SVM 0.787 (0.74–0.83) 0.785 (0.74–0.83) 0.795 (0.75–0.84) 0.785 (0.74–0.83) 0.585 (0.53–0.64) 0.809 (0.77–0.85) 0.903 (0.87–0.93) 0.762 (0.72–0.81) 0.744 (0.70–0.79) 0.786 (0.74–0.83)
 MLP 0.785 (0.74–0.83) 0.770 (0.72–0.82) 0.792 (0.75–0.84) 0.772 (0.73–0.82) 0.607 (0.55–0.66) 0.735 (0.69–0.78) 0.887 (0.85–0.92) 0.809 (0.77–0.85) 0.747 (0.70–0.79) 0.772 (0.73–0.82)
 1D-CNN 0.779 (0.73–0.82) 0.783 (0.74–0.83) 0.782 (0.74–0.83) 0.784 (0.74–0.83) 0.657 (0.61–0.71) 0.784 (0.74–0.83) 0.846 (0.81–0.88) 0.784 (0.74–0.83) 0.752 (0.71–0.80) 0.784 (0.74–0.83)
12 Features (Lifestyle-related features added)
 Decision Tree 0.722 (0.67–0.77) 0.765 (0.72–0.81) 0.724 (0.68–0.77) 0.765 (0.72–0.81) 0.570 (0.52–0.62) 0.776 (0.73–0.82) 0.803 (0.76–0.85) 0.755 (0.71–0.80) 0.686 (0.64–0.74) 0.765 (0.72–0.81)
 Gaussian NB 0.775 (0.73–0.82) 0.766 (0.72–0.81) 0.774 (0.73–0.82) 0.766 (0.72–0.81) 0.685 (0.64–0.74) 0.773 (0.73–0.82) 0.820 (0.78–0.86) 0.759 (0.71–0.81) 0.753 (0.71–0.80) 0.766 (0.72–0.81)
 KNN 0.738 (0.69–0.78) 0.780 (0.73–0.82) 0.743 (0.70–0.79) 0.782 (0.74–0.83) 0.551 (0.50–0.60) 0.879 (0.84–0.91) 0.842 (0.80–0.88) 0.685 (0.63–0.73) 0.696 (0.65–0.75) 0.782 (0.74–0.83)
 XGBoost 0.778 (0.73–0.82) 0.834 (0.79–0.87) 0.782 (0.74–0.83) 0.834 (0.79–0.87) 0.622 (0.57–0.67) 0.837 (0.8–0.88) 0.863 (0.83–0.90) 0.832 (0.79–0.87) 0.743 (0.70–0.79) 0.834 (0.79–0.87)
 RF 0.791 (0.75–0.83) 0.838 (0.80–0.88) 0.795 (0.75–0.84) 0.838 (0.80–0.88) 0.635 (0.58–0.69) 0.850 (0.81–0.89) 0.876 (0.84–0.91) 0.826 (0.79–0.87) 0.756 (0.71–0.80) 0.838 (0.80–0.88)
 Logistic R 0.785 (0.74–0.83) 0.779 (0.73–0.82) 0.792 (0.75–0.84) 0.779 (0.73–0.82) 0.595 (0.54–0.65) 0.791 (0.75–0.83) 0.893 (0.86–0.93) 0.767 (0.72–0.81) 0.744 (0.70–0.79) 0.779 (0.73–0.82)
 SVM 0.790 (0.75–0.83) 0.783 (0.74–0.83) 0.797 (0.75–0.84) 0.783 (0.74–0.83) 0.605 (0.55–0.66) 0.796 (0.75–0.84) 0.894 (0.86–0.93) 0.770 (0.72–0.82) 0.750 (0.70–0.80) 0.783 (0.74–0.83)
 MLP 0.772 (0.73–0.82) 0.797 (0.75–0.84) 0.778 (0.73–0.82) 0.798 (0.75–0.84) 0.619 (0.57–0.67) 0.790 (0.75–0.83) 0.859 (0.82–0.90) 0.806 (0.76–0.85) 0.739 (0.69–0.79) 0.798 (0.75–0.84)
 1D-CNN 0.771 (0.73–0.82) 0.770 (0.72–0.82) 0.776 (0.73–0.82) 0.774 (0.73–0.82) 0.635 (0.58–0.69) 0.861 (0.82–0.90) 0.848 (0.81–0.89) 0.688 (0.64–0.74) 0.742 (0.69–0.79) 0.775 (0.73–0.82)
20 Features (Biochemical measurements added)
 Decision Tree 0.743 (0.70–0.79) 0.777 (0.73–0.82) 0.743 (0.70–0.79) 0.778 (0.73–0.82) 0.631 (0.58–0.68) 0.797 (0.75–0.84) 0.801 (0.76–0.84) 0.758 (0.71–0.80) 0.716 (0.67–0.76) 0.778 (0.73–0.82)
 Gaussian NB 0.786 (0.74–0.83) 0.759 (0.71–0.81) 0.795 (0.75–0.84) 0.762 (0.72–0.81) 0.577 (0.52–0.63) 0.646 (0.59–0.70) 0.906 (0.87–0.94) 0.878 (0.84–0.91) 0.741 (0.69–0.79) 0.762 (0.72–0.81)
 KNN 0.748 (0.70–0.79) 0.787 (0.74–0.83) 0.756 (0.71–0.80) 0.788 (0.74–0.83) 0.540 (0.49–0.59) 0.871 (0.83–0.91) 0.866 (0.83–0.90) 0.705 (0.66–0.75) 0.703 (0.65–0.75) 0.788 (0.74–0.83)
 XGBoost 0.801 (0.76–0.84) 0.851 (0.81–0.89) 0.804 (0.76–0.85) 0.851 (0.81–0.89) 0.662 (0.61–0.71) 0.859 (0.82–0.9) 0.877 (0.84–0.91) 0.843 (0.8–0.88) 0.769 (0.72–0.81) 0.851 (0.81–0.89)
 RF 0.815 (0.77–0.86) 0.843 (0.80–0.88) 0.818 (0.78–0.86) 0.844 (0.80–0.88) 0.690 (0.64–0.74) 0.857 (0.82–0.89) 0.883 (0.85–0.92) 0.831 (0.79–0.87) 0.786 (0.74–0.83) 0.844 (0.80–0.88)
 Logistic R 0.812 (0.77–0.85) 0.804 (0.76–0.85) 0.818 (0.78–0.86) 0.804 (0.76–0.85) 0.638 (0.59–0.69) 0.812 (0.77–0.85) 0.910 (0.88–0.94) 0.796 (0.75–0.84) 0.774 (0.73–0.82) 0.804 (0.76–0.85)
 SVM 0.811 (0.77–0.85) 0.810 (0.77–0.85) 0.817 (0.78–0.86) 0.810 (0.77–0.85) 0.636 (0.58–0.69) 0.831 (0.79–0.87) 0.909 (0.88–0.94) 0.790 (0.75–0.83) 0.773 (0.73–0.82) 0.810 (0.77–0.85)
 MLP 0.807 (0.76–0.85) 0.811 (0.77–0.85) 0.812 (0.77–0.85) 0.812 (0.77–0.85) 0.638 (0.59–0.69) 0.836 (0.80–0.88) 0.901 (0.87–0.93) 0.787 (0.74–0.83) 0.770 (0.72–0.81) 0.812 (0.77–0.85)
 1D-CNN 0.799 (0.76–0.84) 0.814 (0.77–0.86) 0.803 (0.76–0.85) 0.815 (0.77–0.86) 0.662 (0.61–0.71) 0.807 (0.76–0.85) 0.875 (0.84–0.91) 0.822 (0.78–0.86) 0.768 (0.72–0.81) 0.815 (0.77–0.86)

Presented are the results before (Original) and after (SMOTE) applying the synthetic minority oversampling technique

AUC Area under the receiver operating characteristic curve, Gaussian NB Gaussian naïve bayes classifier, KNN K-nearest neighbor, XGBoost Extreme gradient boosting, Logistic R Logistic regression, RF Random forest, SVM Support vector machine, MLP Multilayer perceptron, 1D-CNN 1-dimensional convolutional neural network