Authors : Mohammad Fahad; Majid Hussain; Mohd Arif Khan; Ehteshaam Hussain

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

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Google Scholar : https://tinyurl.com/75bv38s6

Scribd : https://tinyurl.com/jev9f847

DOI : https://doi.org/10.38124/ijisrt/25apr596

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

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Abstract : Diabetes mellitus is one of the most pressing global health issues, affecting millions worldwide. Early prediction and timely management can significantly reduce the disease's impact and improve the quality of life for individuals at risk. This research presents a detailed and human- centric approach to building a diabetes prediction model using machine learning algorithms. By leveraging real-world patient data, we explore various supervised learning techniques, assess their accuracy, and highlight the importance of interpretability in predictive healthcare. This paper emphasizes the ethical implications, real-world applications, and the need to bridge the gap between technology and patient-centered care.

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