Support Vector Machine for Accurate Classification of Diabetes Risk Levels

Abstract

This research explores the application of Support Vector Machines (SVM) for accurately classifying diabetes risk levels based on a publicly available dataset containing 768 instances and 9 attributes, including glucose levels, BMI, blood pressure, and insulin levels. The model's systematic development process involved data preprocessing, feature selection, and hyperparameter optimization to ensure robust performance. Results indicate an overall accuracy of 76%, with high precision and recall for the non-diabetic risk class, but relatively lower performance for the diabetic risk class, highlighting the challenges posed by class imbalance and overlapping data features. To address these issues, future research should incorporate advanced resampling techniques, refined feature engineering, and alternative machine learning models like Random Forest or XGBoost. This research underscores the potential of SVM as a valuable tool for early diabetes detection, offering healthcare professionals a reliable means to identify at-risk individuals and personalize intervention strategies. By bridging theoretical advancements and practical applications, the research contributes to enhancing predictive analytics in medical diagnostics, paving the way for improved patient outcomes and efficient public health management