Diabetes Prediction using Machine Learning Model: A Comparative Approach

Author(s)	Akshay Bhardwaj, Rajesh Chauhan, Devansh Khajuria
Country	India
Abstract	Diabetes mellitus is a disease that affects more than 537 million people worldwide, with millions more undiagnosed until serious complications have already occurred. Traditional diagnostic approaches heavily depend upon laboratory investigation and clinical expertise, which might not always be readily available, particularly in resource-limited healthcare settings. Therefore, machine learning has been proposed as a promising approach for early prediction of diabetes by automating the risk assessment from clinical and demographic data [1], [2], [3]. In this study, six supervised learning models were developed and compared for diabetes prediction using a dataset of 100k patients' records with eight clinical features, including gender, age, hypertension, smoking history, heart disease, BMI, HbA1c level, and blood glucose level.
Keywords	Diabetes, Machine Learning, Classification, Random Forest, Gradient Boosting, SMOTE, HbA1c, Blood Glucose, Healthcare Prediction
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-29

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International Journal For Multidisciplinary Research