Design and Implementation of a Diabetes Prediction System Using Classification Algorithms
Computer Science — 2025, Undergraduate
This study designed and implemented a Diabetes Prediction System that estimates the risk of diabetes from screening indicators such as glucose level, body mass index, and age. Early risk identification supports timely lifestyle and clinical interventions. The system applies classification algorithms to screening data and presents risk levels through a user-friendly interface. Built with Python Django and SQLite, the system was evaluated using a clinical screening dataset. Findings showed strong classification performance in identifying high-risk individuals. The study recommends adoption in community screening programmes and health awareness initiatives.
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