Diabetes Prediction and Management using Machine Learning Algorithms

Abstract: Our research introduces a multifaceted diabetes prediction and management system, employing two distinct methodologies: one based on fasting and postprandial blood sugar levels, ie, without the datasets, and the other incorporating additional health parameters like glucose levels, blood pressure, BMI, and age, with datasets utilizing logistic regression. Evaluation of both approaches demonstrated robustpredictive capabilities. A user-friendly website interface facilitates seamless data input, enhancing accessibility for users. Complementing predictive features, an online communityplatform was established to foster peer support and informationexchange among individuals managing diabetes, promoting a sense of community and shared experience. Moreover, the system generates personalized diet plans tailored to users' diabetes status, providing actionable dietary guidance to support health management goals. By integrating predictive analytics, user engagement, and personalized dietary support, our system aims to empower individuals with diabetes, facilitating better health outcomes and fostering a supportive environment for effective disease management.