Santanu Basak; Angshuman Khan; Mayank Raj; Abhishek Pandey

Predicting Diabetes Using Machine Learning: Models and Insights

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Author(s)

Santanu Basak ¹ Angshuman Khan ^2,* Mayank Raj ¹ Abhishek Pandey ¹

1. Department of Computer Science & Engineering, University of Engineering & Management Jaipur, Jaipur, Rajasthan 303807, India

2. Department of Electronics & Communication Engineering, University of Engineering & Management Jaipur, Jaipur, Rajasthan 303807, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2026.02.12

Received: 21 Mar. 2025 / Revised: 24 May 2025 / Accepted: 8 Jan. 2026 / Published: 8 Apr. 2026

Index Terms

Diabetes Prediction, Machine Learning Models, Support Vector Machine (SVM), Medical Data Classification, Classification Performance Metrics

Abstract

Diabetes mellitus is a chronic metabolic disorder with a rapidly increasing global prevalence, posing a significant public health challenge. Early detection of diabetes can enable timely intervention and preventive measures, thereby reducing the risk of long-term complications. In this study, a machine learning (ML)-based methodology is proposed for the early prediction of diabetes mellitus. The proposed approach enhances existing prediction systems by improving key performance metrics, including precision, recall, and F1-score, and achieves an efficiency improvement of 4%–10% compared to state-of-the-art methods. Experimental results demonstrate that the support vector machine outperforms other ML algorithms for diabetes prediction, achieving 92% accuracy, 95% precision, 92% recall, 93% F1-score, 92% specificity, and an area under the receiver operating characteristic curve of 0.97.

Cite This Paper

Santanu Basak, Angshuman Khan, Mayank Raj, Abhishek Pandey, "Predicting Diabetes Using Machine Learning: Models and Insights", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.2, pp. 192-204, 2026. DOI:10.5815/ijieeb.2026.02.12

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