## 3 Results and Discussion
### Model Evaluation and Performance
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Subset A: Age + Gender + Tg-Index**} \\ \hline
**Model** & **Accuracy** & **Precision** & **Recall (Sensitivity)** & **Specificity** & **F-Score** & **AUC** \\ \hline Random forest & Train & Test & & & & & \\ Logistic regression & 0.78 & 0.77 & 0.80 & 0.73 & 0.82 & 0.76 & 0.86 \\ Logistic regression & 0.76 & 0.75 & 0.76 & 0.74 & 0.77 & 0.83 \\ XGB classifier & 0.84 & 0.82 & 0.83 & 0.81 & 0.84 & 0.82 & 0.90 \\ \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Subset B: Age + Gender + TG/HDL-C ratio**} \\ \hline
**Model** & **Accuracy** & **Precision** & **Recall (Sensitivity)** & **Specificity** & **F-Score** & **AUC** \\ \hline Random forest & Train & Test & & & & \\ Random forest & 0.78 & 0.77 & 0.78 & 0.74 & 0.79 & 0.76 & 0.86 \\ Logistic regression & 0.76 & 0.75 & 0.75 & 0.76 & 0.74 & 0.75 & 0.83 \\ XGB classifier & 0.86 & 0.84 & 0.83 & 0.86 & 0.82 & 0.85 & 0.92 \\ \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Subset C: Age + Gender + PAI**} \\ \hline
**Model** & **Accuracy** & **Precision** & **Recall (Sensitivity)** & **Specificity** & **F-Score** & **AUC** \\ \hline \multirow{3}{*}{Random forest Logistic regression XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoostoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoostoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoostoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoostoost XGBoostoost XGBoost XGBoostoost XGBoost XGBoostoost XGBoost XGBoost XGBoostoost XGBoost XGBoost XGBoost XGBoost XGBoostoost XGBoostoost XGBoost XGBoostoost XGBoost XGBoost XGBoostoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoost XGBoostoost XGBoostoost XGBoost XGBoost XGBoost XGBoostoost XGBoost XGBoostoost XGBoostoost XGBoostoost XGBoost XGBoostoost XGBoostoost XGBoostoost XGBoostoost XGBoostoost XGBoostoost XGBoostostoost XGBoostoostoost XGBoostoost XGBoostostoost XGBoostoost XGBoostoostoost XGBoostoostoost XGBoostoostoost XGBoostoostoostoost XGBoostoostoostoostoostoostoostoostoostoostostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostoostostoostoostoostoostoostostTo further optimize the model and attain optimal performance, we altered the imputation variables by including creatinine into the trained subsets (A, B and C).