###### Abstract  
Diabetes mellitus (DM) is a global health challenge that requires advanced strategies for its early detection and prevention. This study evaluates the South Korean population using the Korea National Health and Nutrition Examination Survey (KNHANES) dataset from 2015 to 2021, provided by the Korea Disease Control and Prevention Agency (KDCA), focusing on improving diabetes prediction models. Outlier removal was implemented using Mahalanobis distance (MAH), and feature selection was based on multicillinearity (MC) and reliability analysis (RA). The proposed Extreme Gradient Boosting (XGBoost) model demonstrated exceptional performance, achieving an accuracy of 98.04% (95% CI: 97.89...98.59), an FI-score of 98.24%, and an Area Under the Curve (AUC) of 98.71%, outperforming other state-of-the-art models. The study highlights the significance of rigorous outlier detection and feature selection in enhancing the predictive power of diabetes risk models. Notably, a significant increase in diabetes cases was observed during the COVID-19 pandemic, particularly linked to male sex, older age, rural location, hypertension, and obesity, underscoring the need for enhanced public health strategies for early intervention and targeted prevention. diabetes; data integration; outlier remover; Mahalanobis distance; healthcare; disease prevention; COVID-19 +
Footnote †: journal: Medical  
+++ ==WARNING: Truncated because of repetitions==
prediction framework that utilizes Random Forest (RF), LightGBM, Glmnet, XGBoost [5; 6; 7; 8], and the LS-SVM classifier to address prevalent data quality issues in medical datasets, such as outliers. The framework achieved a classification accuracy of 96.57% [9]. Furthermore, [10] presented a selective data preprocessing approach using the Synthetic Minority Oversampling Technique (SMOTE) to balance the training data and manage outliers. Advanced predictive models that integrate these diverse risk factors can enhance early detection. However, their performance is frequently compromised by the presence of outliers. Outliers, which are data points that deviate significantly from most datasets, can result from measurement errors, data entry mistakes, or inherent population variability. The presence of outliers can distort statistical analyses and predictive models, leading to unreliable outcomes and reduced predictive accuracy [11; 12]. Effective outlier detection and removal are crucial for improving the data quality and predictive model performance. Mahalanobis distance (MAH), a multivariate distance measure, offers a robust method for outlier detection by accounting for correlations among variables and the overall covariance structure of the data [13]. The Mahalanobis distance method was utilized to generate values in patients with type 2 diabetes from NHANES to 1999-2018, using feature selection, as described in [14]. In this study, we leveraged Mahalanobis-distance-based outlier removal to enhance diabetes prediction models using the Korea National Health and Nutrition Examination Survey (KnthanES) dataset from 2015 to 2021 provided by the Korea Disease Control and Prevention Agency (KDCA) [15; 16; 17]. To address the complex interplay between the risk factors, we implemented feature selection based on multicollinearity (MC) [18; 19] and reliability analysis (RA) [20] to refine the predictive variables. We applied this enhanced dataset to several machine-learning classifiers, including Extreme Gradient Boosting (XGBoost), Naive Bayes (NB), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Trees (DTs) [21]. The objective was to develop a comprehensive risk assessment model that integrates demographic, genetic, biochemical, and lifestyle variables, while effectively addressing the issue of outliers. Our empirical analysis demonstrates that the proposed approach significantly improves the model accuracy and robustness across all classifiers, with XGBoost showing superior performance.