Feature Correlation and Class Balancing Effects on Random Forest Accuracy in Diabetes Risk Prediction Using the Teboul Health Indicators Dataset

Abstract

Early detection of diabetes is essential for reducing disease burden and long-term healthcare costs. This study presents a Random Forest-based predictive framework for diabetes risk assessment using the Teboul Health Indicators Dataset, which is derived from the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS 2015). The proposed framework incorporates feature correlation analysis and evaluates the effects of four class-balancing strategies, namely under-sampling, oversampling, Synthetic Minority Oversampling Technique (SMOTE), and class weighting, on classification performance. The dataset, comprising 253,680 records and 21 health-related attributes, was preprocessed using normalization and stratified random sampling. Correlation analysis identified General Health (GenHlth), High Blood Pressure (HighBP), Body Mass Index (BMI), and Age as the most influential predictors of diabetes risk. Experimental results demonstrated that the under-sampling approach achieved the best trade-off between precision and sensitivity, yielding an F1-score of 0.426, a recall of 0.777, and an area under the receiver operating characteristic curve (AUC) of 0.808, outperforming the other balancing methods. The findings indicate that appropriate class balancing improves sensitivity and fairness in diabetes classification, while Random Forest provides model interpretability through feature-importance analysis. Future work will focus on integrating Explainable Artificial Intelligence (XAI) techniques and hybrid ensemble balancing methods to further enhance transparency and predictive performance in healthcare analytics.