## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Risk** & **Precision** & **Recall** & **F1** \\ \hline Logistic Regression– & & High Risk & 0.79 & 0.64 & 0.71 \\ Gaussian Naive Bayes & 57.6\% & Low Risk & 0.52 & 0.91 & 0.66 \\  & & Mid Risk & 0.56 & 0.18 & 0.28 \\ \hline SVM & 59.6\% & High Risk & 0.9 & 0.6 & 0.72 \\  & & Low Risk & 0.52 & 0.93 & 0.67 \\  & & Mid Risk & 0.61 & 0.25 & 0.36 \\ \hline Gradient Boosting & 73.9\% & High Risk & 0.80 & 0.83 & 0.81 \\  & & Low Risk & 0.72 & 0.74 & 0.73 \\  & & Mid Risk & 0.72 & 0.68 & 0.7 \\ \hline Decision Trees & 80.3\% & High Risk & 0.87 & 0.85 & 0.86 \\  & & Low Risk & 0.86 & 0.74 & 0.79 \\  & & Mid Risk & 0.73 & 0.84 & 0.78 \\ \hline Random Forest & 81.3\% & High Risk & 0.87 & 0.85 & 0.86 \\  & & Low Risk & 0.86 & 0.76 & 0.81 \\  & & Mid Risk & 0.74 & 0.84 & 0.79 \\ \hline XGBost & 84.2\% & High Risk & 0.85 & 0.87 & 0.86 \\  & & Low Risk & 0.87 & 0.82 & 0.85 \\  & & Mid Risk & 0.81 & 0.84 & 0.83 \\ \hline \multirow{3}{*}{CatBoost} & \multirow{3}{*}{84.7\%} & High Risk & 0.94 & 0.87 & 0.91 \\  & & Low Risk & 0.83 & 0.88 & 0.85 \\ \cline{1-1}  & & Mid Risk & 0.80 & 0.79 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of machine learning models with random sampling. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Risk** & **Precision** & **Recall** & **F1** \\ \hline Logistic Regression– & & High Risk & 0.91 & 0.91 & 0.91 \\ Gaussian Naive Bayes & 65.5\% & Low Risk & 0.86 & 0.90 & 0.88 \\  & & Mid Risk & 0.84 & 0.79 & 0.82 \\ \hline SVM & 61.5\% & High Risk & 0.97 & 0.60 & 0.74 \\  & & Low Risk & 0.53 & 0.94 & 0.68 \\  & & Mid Risk & 0.64 & 0.24 & 0.35 \\ \hline Gradient Boosting & 81.3\% & High Risk & 0.93 & 0.93 & 0.93 \\  & & Low Risk & 0.76 & 0.89 & 0.82 \\  & & Mid Risk & 0.79 & 0.63 & 0.70 \\ \hline Decision Trees & 85.7\% & High Risk & 0.91 & 0.91 & 0.91 \\  & & Low Risk & 0.84 & 0.85 & 0.85 \\  & & Mid Risk & 0.83 & 0.82 & 0.83 \\ \hline Random Forest & 85.7\% & High Risk & 0.91 & 0.89 & 0.90 \\  & & Low Risk & 0.86 & 0.89 & 0.87 \\  & & Mid Risk & 0.82 & 0.79 & 0.88 \\ \hline XGBoost & 86.7\% & High Risk & 0.94 & 0.91 & 0.93 \\  & & Low Risk & 0.85 & 0.90 & 0.87 \\  & & Mid Risk & 0.83 & 0.79 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of machine learning models with stratified sampling. categories, particularly mid-risk cases (F1 score improvement from 0.28 to 0.82). Support vector machines (SVM) exhibit a modest accuracy increase from 59.6% to 61.5. However, the model efficacy in predicting the mid-risk cases does not improve, as reflected by the low F1 score (0.36 in the first table to 0.35 in the second). The performance of the gradient boosting models also increased significantly from 73.9% to 81.3%, with improved precision and recall for high-risk cases (F1 score improving from 0.81 to 0.93) and low-risk cases (F1 score improving from 0.73 to 0.82). Decision trees also show a substantial performance improvement, with accuracy increasing from 80.3% to 85.7% and robust precision, recall, and F1 scores across all risk levels, particularly mid-risk cases, where the F1 score rose from 0.78 to 0.83. Similarly, the performance of random forest models also improved significantly from 81.3% to 85.7%, with gains in low-risk precision (0.86 to 0.89) and mid-risk recall (0.84 to 0.79). As observed with random sampling, XGBoost and CatBoost remain the best-performing models. However, unlike the results from random sampling, XGBoost achieves the highest accuracy among all models, increasing from 84.2% to 86.7%, while CatBoost maintained its accuracy at 84.7%. XGBoost showed a significant increase in the F1 score for high-risk cases (from 0.86 to 0.93) and consistently robust performance across all risk categories. Overall, except for CatBoost, all the other models demonstrated significant performance improvements using stratified sampling.