AI – Based Early Diagnosis & Prevention of Diabetes

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Year : 2026 | Volume : 13 | Issue : 02 | Page :
    By

    Swati Dixit,

  • Mahi Singh Chauhan,

  • Padmini Mishra,

  • Archana Dwivedi,

  1. Student, Department of Computer Science & Engineering, Babu Banarasi Das Institute of Technology & Management, Lucknow, Uttar Pradesh, India
  2. Student, Student, Department of Computer Science & Engineering, Babu Banarasi Das Institute of Technology & Management, Lucknow, Uttar Pradesh, India
  3. Assistant Professor, Department of Computer Science & Engineering, Babu Banarasi Das Institute of Technology & Management, Lucknow, Uttar Pradesh, India
  4. Assistant Professor, Department of Computer Science & Engineering, Babu Banarasi Das Institute of Technology & Management, Lucknow, Uttar Pradesh, India

Abstract

The worldwide burden of Diabetes Mellitus, especially Type 2 diabetes (T2D) has escalated to a critical level. Early detection of diabetes is essential to reduce long‑term complications and healthcare costs. This study explores the use of artificial intelligence (AI) techniques to improve the early diagnosis and prevention of diabetes. We developed an AI model using the Random Forest algorithm, the model predicts diabetes risk based on clinical and lifestyle variables and identifies high‑risk individuals for targeted preventive interventions. We investigate the contribution of Explainable AI (XAI) techniques, including SHAP and LIME, to enhance clinician interpretability and trust. Performance was evaluated with metrics such as accuracy, sensitivity, and AUC, and compared to conventional risk‑scoring approaches. The proposed system improved predictive performance over traditional methods. These findings suggest that AI‑driven tools can support clinicians in early risk stratification and may facilitate personalised prevention strategies for diabetes in diverse populations.

Keywords: Diabetes Mellitus, Type 2 Diabetes (T2D), Early Diagnosis, Artificial Intelligence, Machine Learning, Random Forest, Explainable AI, SHAP, LIME, Predictive Analytics, Lifestyle Risk Factors, Personalized Prevention.

[This article belongs to Research & Reviews: A Journal of Bioinformatics ]

How to cite this article:
Swati Dixit, Mahi Singh Chauhan, Padmini Mishra, Archana Dwivedi. AI – Based Early Diagnosis & Prevention of Diabetes. Research & Reviews: A Journal of Bioinformatics. 2026; 13(02):-.
How to cite this URL:
Swati Dixit, Mahi Singh Chauhan, Padmini Mishra, Archana Dwivedi. AI – Based Early Diagnosis & Prevention of Diabetes. Research & Reviews: A Journal of Bioinformatics. 2026; 13(02):-. Available from: https://journals.stmjournals.com/rrjobi/article=2026/view=242800


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Regular Issue Subscription Original Research
Volume 13
Issue 02
Received 13/04/2026
Accepted 24/04/2026
Published 04/05/2026
Publication Time 21 Days
23

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