Diabetes Risk Prediction Through Fine-Tuned Gradient Boosting

Diabetes, a chronic metabolic disease with a rising global prevalence, significantly impacts individuals’ health. Diabetes increases a person’s risk of developing various diseases, including heart disease, stroke, vision problems, nerve damage, etc. Early detection and proactive care of diabetes can lessen its impact and improve patient outcomes. Utilizing the powers of machine learning algorithms in the medical field has shown significant promise in accurately identifying diseases and implementing customized treatments, reducing the workload of healthcare professionals. This paper proposes a methodology based on Gradient Boosting technique to accurately predict diabetes. This study also provides a thorough analysis of diabetes prediction using a variety of classifiers, including Linear Discriminant Analysis (LDA), Extra Tree Classifiers (ETC), Quadratic Discriminant Analysis (QDA), Stochastic Gradient Descent (SGD), Bayesian Gradient Descent Classifiers (BGC), and Gradient Boosting (GB) classifiers. The pre-processing methods of Standard Scalar Normalization and Synthetic Minority Over-sampling Technique (SMOTE) are used to improve the predictive models’ quality. SMOTE is used for class balancing. Accuracies achieved by LDA, ETC, QDA, SGD, BGC, and GB are 77.34%, 74.20%, 73.50%, 75.01%, 74.08%, and 80.19%, respectively. The authors optimized the Gradient Boosting (GB) classifier through a rigorous grid search optimization process to maximize performance, yielding an accuracy of 82.70%.