Diabetes Mellitus is a chronic condition affecting millions worldwide, associated with factors like age, body mass index, blood pressure, and social determinants such as income level, education, and healthcare access. This study uses a mix of these factors derived from a public health survey to train machine learners for diabetes prediction. The data includes 29 features and 223,022 records. A key goal here is to investigate levels of feature importance in risk factors to assess the impact of social determinants on diabetes. We employ six machine learning models, including XGBoost, AdaBoost, LightGBM, Random Forest, Naive Bayes, and Logistic Regression, and utilize SHapley Additive exPlanations to measure feature importance. Predictive performance metrics include accuracy, precision, recall, and the area under the receiver operating characteristic curve. Empirical results show that five out of six models achieved 85% accuracy, with blood pressure, body mass index, cholesterol, weekly alcohol consumption, and time since the last checkup being the most significant predictive attributes. These initial findings highlight the potential of machine learners to predict diabetes and contribute to early monitoring of the identified risk factors. In related future research, a planned work will investigate whether identifying and incorporating other factors would improve overall predictive performance.
