STM Journals Peer-reviewed research from Consortium e-Learning Network

Research & Reviews: A Journal of Bioinformatics Original Research

AI-Based Early Diagnosis & Prevention of Diabetes

  1. Swati Dixit Department of Computer Science & Engineering, Babu Banarasi Das Institute of Technology & Management, Lucknow
  2. Mahi Singh Chauhan Student, Department of Computer Science & Engineering, Babu Banarasi Das Institute of Technology & Management, Lucknow
  3. Padmini Mishra Department of Computer Science & Engineering, Babu Banarasi Das Institute of Technology & Management, Lucknow
  4. Archana Dwivedi Department of Computer Science & Engineering, Babu Banarasi Das Institute of Technology & Management, Lucknow

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

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