A DATA-DRIVEN PARADIGM FOR FORECASTING TYPE 2 DIABETES RISK THROUGH THE INTEGRATION OF MACHINE LEARNING TECHNIQUES AND CLINICAL DATA WITHIN A UNIFIED WEB-BASED PLATFORM
DOI:
https://doi.org/10.69980/gfs9k223Keywords:
SDG-3, diabetes detection, feature engineering, personalized healthcare solution, XGBoost, machine learning, deep learningAbstract
Type 2 diabetes mellitus (T2DM) has emerged as one of the most significant metabolic disorders affecting populations globally, necessitating the development of sophisticated early screening methodologies. The integration of artificial intelligence and data science in clinical applications has created unprecedented opportunities for improving disease prediction accuracy while advancing the United Nations Sustainable Development Goal 3 (Good Health and Well-Being). This research endeavors to establish a robust predictive framework by systematically evaluating multiple classification algorithms across four distinct diabetes- related datasets obtained from Kaggle and the UCI Machine Learning Repository. Our comprehensive analysis encompasses eight traditional machine learning classifiers alongside three deep neural network architectures, with model effectiveness measured using standard evaluation metrics including Accuracy, Precision, Recall, F1-Score, and Area Under the ROC Curve (AUC). We introduce a dimensionality reduction strategy based on Principal Component Analysis (PCA) to optimize feature representation, comparing i ts effectiveness against LASSO-based feature selection. Our experimental findings demonstrate that the XGBoost classifier combined with PCA-based feature engineering delivers exceptional predictive capability, achieving approximately 97% accuracy and F1 -score with a 96% AUC across the diabetes datasets. The optimized model has been deployed within an intuitive web-based application designed to facilitate accessible diabetes risk assessment. Additionally, we propose an integrated digital health framework incorporating Internet of Medical Things (IoMT), Robotic Process Automation (RPA), and AI technologies to enhance the reliability and scalability of personalized diabetes management solutions.
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