A Machine Learning-Based System for Early Diabetes Prediction using Medical Data
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A Machine Learning-Based System for Early Diabetes Prediction using Medical Data
Eshwar Palle, Tushar Bhandurge, Vikrant Satav
eshwarpalle123@gmail.com , tusharbhandurge8@gmail.com , vikrantsatav4@grmail.com,
Department of Computer Science, MIT ADT University, Pune, India
Abstract:Diabetes is one of the most common chronic diseases affecting millions of people worldwide. Early detection and prediction of diabetes can significantly reduce the risk of severe complications such as heart disease, kidney failure, and nerve damage. Traditional diagnostic methods rely on medical tests and physician analysis, which may delay early identification. Machine Learning (ML) techniques provide powerful tools for analysing medical datasets and predicting disease occurrence based on patterns and patient health parameers. This research presents a machine learning-based framework for predicting diabetes using clinical and lifestyle-related data. The study evaluates several supervised learning algorithms including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines. The proposed system aims to improve prediction accuracy and support healthcare professionals in early diagnosis and preventive treatment planning. Keywords—Machine Learning, Diabetes Prediction, Healthcare Analytics, Data Intelligence, Disease Prediction
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