Diabetes Risk & Al Nutrition Assistant

Year : 2026 | Volume : 13 | Issue : 01 | Page : 31 38
    By

    Shivin Tarare,

  • Saurabh Paraskar,

  • Kiran Chadde,

  • Himanshu Popat,

  • Milind Deshpande,

  1. Research Scholar, Department of Computer Application, P. E. S. Modern College of Engineering, Pune, Maharashtra, India
  2. Research Scholar, Department of Computer Application, P. E. S. Modern College of Engineering, Puneq, Maharashtra, India
  3. Research Scholar, Department of Computer Application, P. E. S. Modern College of Engineering, Pune, Maharashtra, India
  4. Research Scholar, Department of Computer Application, P. E. S. Modern College of Engineering, Pune, Maharashtra, India
  5. Research Scholar, Department of Computer Application, P. E. S. Modern College of Engineering, Pune, Maharashtra, India

Abstract

The rising prevalence of diabetes mellitus has emerged as a major global health challenge. Early identification of individuals at risk, combined with personalized lifestyle-based interventions, can significantly reduce future complications. This study presents an AI-driven Nutrition Assistant integrated with a Diabetes Risk Prediction model. The system uses a machine learning classification approach to estimate the likelihood of diabetes based on clinical and nutritional factors, including body mass index, glucose levels, diet patterns, and physical activity. Data preprocessing, feature selection, and model training were performed on structured datasets to ensure reliability and accuracy. The backend, developed using Node.js and Prisma ORM, manages data efficiently, while the AI module employs OpenAI APIs to provide personalized dietary recommendations. Experimental results demonstrate high prediction accuracy and strong user engagement in nutrition guidance. The proposed system highlights the combined potential of predictive analytics and AI-assisted nutrition planning for supporting preventive healthcare.

Keywords: AI nutrition assistant, diabetes prediction, dietary recommendations, machine learning, predictive analytics, preventive healthcare

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

How to cite this article:
Shivin Tarare, Saurabh Paraskar, Kiran Chadde, Himanshu Popat, Milind Deshpande. Diabetes Risk & Al Nutrition Assistant. Research & Reviews: A Journal of Bioinformatics. 2026; 13(01):31-38.
How to cite this URL:
Shivin Tarare, Saurabh Paraskar, Kiran Chadde, Himanshu Popat, Milind Deshpande. Diabetes Risk & Al Nutrition Assistant. Research & Reviews: A Journal of Bioinformatics. 2026; 13(01):31-38. Available from: https://journals.stmjournals.com/rrjobi/article=2026/view=239699


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Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 30/12/2025
Accepted 05/02/2026
Published 27/03/2026
Publication Time 87 Days
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