Predicting Diabetes in Adults: Identifying Important Features in Unbalanced Data Over a 5-Year Cohort Study Using Machine Learning Algorithm

Abstract

Background Imbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data from the Fasa Adult Cohort Study (FACS) with a 5-year follow-up of 10,000 participants, we developed predictive models for Type 2 diabetes. Methods We employed various data-level and algorithm-level interventions, including SMOTE, ADASYN, SMOTEENN and KMeans SMOTE, paired with Random Forest, Gradient Boosting, and Multi-Layer Perceptron (MLP). Performance was evaluated using F1 score, AUC, and G-means. Results Our results show that ADASYN with MLP achieved an F1 score of 82.17 ± 3.38, AUC of 89.61 ± 2.09, and G-means of 89.15 ± 2.31. SMOTE with MLP followed closely with an F1 score of 79.85 ± 3.91, AUC of 89.7 ± 2.54, and G-means of 89.31 ± 2.78. The SMOTEENN with Random Forest combination achieved an F1 score of 78.27 ± 1.54, AUC of 87.18 ± 1.12, and G-means of 86.47 ± 1.28. Conclusion These combinations effectively address class imbalance, improving the accuracy and reliability of diabetes predictions. The findings highlight the importance of using appropriate data-balancing techniques in medical data analysis.