## 5 Discussions  
This study significantly advances the field of diabetes risk prediction and prevention by integrating advanced machine-learning techniques with robust outlier detection methods. Specifically, we employed Mahalanobis-distance-based outlier removal to enhance diabetes prediction models using data from the Korea National Health and Nutrition Examination Survey (KNHANES) dataset (2015-2021) provided by the Korea Disease Control and Prevention Agency (KDCA) [15; 16; 17]. We implemented feature selection based on multicollinearity (MC) and reliability analysis (RA) to address the complex interplay between various risk factors effectively. These refined datasets were then applied to several machine-learning classifiers, including Extreme Gradient Boosting (XGBoost), Naive Bayes (NB), K-Nearest Neighbors (KNNs), Random Forest (RF), and Decision Trees (DTs). The primary objective was to develop a comprehensive risk assessment model that integrates demographic, genetic, biochemical, and lifestyle variables, while effectively managing outliers. The empirical results demonstrate that the proposed approach significantly enhances model accuracy and robustness across all classifiers, with XGBoost demonstrating superior performance. The application of Mahalanobis distance (MAH) for outlier removal yielded substantial improvements in the accuracy and reliability of the predictive models. Outliers in clinical datasets often undermine the model performance by distorting the statistical relationships among variables. By effectively identifying and removing these outliers, our study enhanced the precision and dependability of diabetes risk predictions. The MAH-based outlier removal method was particularly effective in refining the dataset, leading to a superior model performance. Notably, the XGBoost model achieved remarkable results, with an accuracy of 98.04%, an F1-score of 98.24%, and an AUC of 98.71%. These metrics underscore the superiority of the MAH-based approach over other state-of-the-art models, highlighting its potential for broader applications in predictive healthcare modeling. The study also involved a comparative analysis of the importance of the features across different models. By comparing the importance scores between XGBoost, Random Forest, and our MC- and RA-based models, we observed similarities and differences in how these models prioritize various features. Some features, such as fasting blood glucose levels, have been consistently identified as critical predictors of diabetes, indicating their strong influence on the risk of diabetes. In contrast, the other features showed varying degrees of importance, depending on the model, reflecting the distinct data-processing mechanisms of each algorithm. These insights into feature importance have practical implications for healthcare providers, who can prioritize the monitoring and management of the most influential features identified by the models. An analysis of the KNHANES data revealed a significant increase in diabetes cases during the COVID-19 pandemic, particularly among males, older individuals, rural residents, and those with hypertension or obesity. This increase in cases is likely attributable to pandemic-induced lifestyle changes such as reduced physical activity, poor diet, and increased psychological stress. These findings underscore the need for targeted public \begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithms** & **Accuracy (\%)** & **AUC (\%)** \\ \hline CatBoost CBC [26] & 85.0 & 93.0 \\ DDLNN [27] & 84.42 & - \\ AB + XB [29] & - & 95.0 \\ RFWBP [30] & 83.0 & 86.0 \\ PCA + ELM [31] & 90.57 & - \\ Proposed method\_MAH\_XGB & 98.04 & 98.71 \\ \hline \hline \end{tabular}  
* MAH_XGB: XGB within Mahalanobis distance. CatBoost CBC: CatBoost Classifier. DDLNN: Deep Dense Layer Neural Network. PCA: Principal Component Analysis. ELM: Extreme Learning Machine. RFWBP: Random forest algorithm with the best parameters. AB + XB: The ensembling of two boosting (adaptive (AB) and gradient (XB))-type classifiers.  
\end{table}