An Intelligent Flask-Based Web Application for Early Diabetes Risk Prediction

Early detection of diabetes can substantially reduce long-term complications and healthcare costs, yet traditional diagnostic pathways remain resource-intensive and in- accessible to many populations. In this work, we present a comprehensive design, implementation, and assessment of a Flask-based web application that leverages a stacked ensemble using artificial intelligence models to forecast individual diabetes risk using routine clinical parameters. The platform integrates secure user authentication, personalized trend visualizations, and an administrative dashboard for populationlevel analytics. We detail our methodology—from data preprocessing and feature engineering to model training and web deployment—evaluate system performance on benchmark and real-world datasets, and discuss the broader implications for scalable preventive healthcare solutions.