Authors : M.V.B.T Santhi; P. Priyanka; T. Jyothi; B. Sai Ganesh; J. Sai Teja

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/nhhja59n

DOI : https://doi.org/10.38124/ijisrt/25apr871

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Diabetes Mellitus is a long-term metabolic disorder impacting millions globally, with its incidence continually increasing. Timely diagnosis and early intervention are vital for effective management, helping to minimize complications and enhance patients' quality of life. This research introduces a predictive framework that utilizes machine learning methods to support the early detection of individuals at risk of developing diabetes. Drawing from a rich dataset that includes demographic, clinical, and lifestyle information, the model integrates advanced algorithms such as Logistic Regression, Decision Trees, and Support Vector Machines to estimate the probability of diabetes onset. The model undergoes thorough testing and validation using real- world data, showcasing strong accuracy and reliability. This provides healthcare professionals with actionable insights for early intervention. By leveraging machine learning, this approach promotes a proactive and tailored strategy for diabetes care, ultimately aiming to enhance patient health outcomes and overall well-being.

Keywords : Diabetes Prediction, Machine Learning Techniques, Predictive Modeling, Deep Learning, Feature Selection, Wearable Sensors, Ethical Considerations.

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