Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{164117,
        author = {Samridhi Puri and Satinder Kaur  and Satveer Kour and Kumari Sarita},
        title = {A REVIEW ON APPLICATIONS OF MACHINE LEARNING TECHNIQUES IN DIABETES HANDLING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {181-184},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164117},
        abstract = {Diabetes mellitus is a chronic metabolic disorder affecting millions of people worldwide. Early diagnosis and effective management of diabetes are crucial to prevent complications and improve patient outcomes. In recent years, the integration of machine learning (ML) techniques has shown promising results in aiding diabetes diagnosis and management. Various Machine learning techniques can be used like Support Vector Machine (SVM), K Nearest Neighbour (KNN), Random Forest, Decision Tree in detection and treatment of diabetes mellitus. This paper provides an overview of different machine learning applications used in diabetes handling. In addition, it also discusses how to use ML in this discipline. It is concluded that ML techniques is a good approach for controlling diabetes mellitus at an early stage.},
        keywords = {Machine learning, Diabetes mellitus, Support Vector Machine, Random Forest },
        month = {},
        }