Integrating RF-GBDT: Optimizing Machine Learning Techniques for Diabetic Prediction Model - Jayasri, N. P.; R. Aruna, R. Aruna; S. Ravikumar, S. Ravikumar; T. Thilagam, T. Thilagam (Journal of Neonatal Surgery, 2025)
Diabetes is a persistent medical condition that has seen a significant rise in incidence in recent years. Because precise datasets are necessary for early prognosis, this presents complications. Big data plays a significant role in predicting diabetes by examining enormous volumes of health-related information. Through advanced analytics, algorithms can identify patterns, risk factors, and correlations in data such as patient demographics, medical history, genetic markers, lifestyle factors, and biomarkers. This research introduces a diabetes prediction model, RF-GBDT classifier, tailored to identify potentially effective peptides against diabetes. Combining sequence data with the Random Forest – Optimized Gradient Boosting Decision Trees (GBDT) framework, RF-GBDT aims to improve the accuracy of antidiabetic peptide prediction. Results demonstrate the model's remarkable performance with an accuracy of 99.8% and an AUC of 95.2%. Furthermore, feature selection techniques streamline prediction times without compromising classifier accuracy. These findings, comparative to existing studies, affirm the efficacy of the proposed method, positioning it as a valuable adjunctive tool in diabetes diagnosis.