Optimizing Diabetes Prediction: An Evaluation of Machine Learning Models Through Strategic Feature Selection

Diabetes, a widespread chronic ailment in the United States, imposes significant economic and health burdens, impacting quality of life and life expectancy. This study analyzes a clinical dataset of 253,680 patients from the Behavioral Risk Factor Surveillance System (BRFSS). The dataset encompasses 21 predictors, including high blood pressure, cholesterol, body mass index (BMI), smoking, stroke, heart disease, physical activity, fruit consumption, vegetable consumption, alcohol consumption, insurance coverage, lack of medical visits due to financial constraints, general health, days with mental health issues, days with physical injuries in the past 30 days, difficulties in walking, gender, age, income, and education level. The objective is to balance the training dataset, compare different supervised machine learning models, and identify critical clinical features contributing to diabetes using unsupervised feature selection methods. A total of 34 machine learning models in MATLAB2023a were trained and compared. Quadratic Support Vector Machine (SVM), Coarse Gaussian SVM, and Narrow Neural Networks achieved the highest training accuracy (76.3%), while the Bilayered Neural Network attained 74.7% on an unseen test dataset. Among all, Quadratic SVM demonstrated the best overall performance based on average accuracy, precision, recall, and F1 score. Feature selection highlighted nine key predictors: high blood pressure, high cholesterol, BMI, heart disease, physical activity, general health, recent bodily injuries, mobility issues, and age. A model trained on these features achieved a commendable accuracy of 75.4%, demonstrating the feasibility of a simplified, efficient diagnostic tool with a diagnostic efficacy of 0.7. This study underscores the potential of streamlined models to predict diabetes with fewer parameters while maintaining high accuracy, offering a valuable tool for healthcare diagnostics.