Department of Computer Science and Engineering (AIML), ACE College of Engineering, Ankushapur, Ghatkesar Mandal, Medchal District, Telangana. – 501301, India.
International Journal of Science and Research Archive, 2025, 14(01), 1252–1263
Article DOI: 10.30574/ijsra.2025.14.1.0183
Received on 06 December 2024; revised on 18 January 2025; accepted on 21 January 2025
Efficient and accurate prediction of diabetes and its related complications is critical for early intervention and better health outcomes. Traditional diagnostic methods often require extensive manual effort and are limited in their predictive capabilities. This system introduces the Comorbid Systematic Health Analyzer (CSHA) an intelligent system designed to leverage advanced machine learning models to diagnose diabetes, assess the risk of comorbid conditions, and provide actionable insights for personalized healthcare. By integrating data from patient surveys and medical reports, CSHA offers a robust solution for healthcare professionals to streamline diagnostic workflows and improve decision-making. This system explores the system’s core components, relevant literature, machine learning methodologies, and the potential for future enhancements.
Diabetes prediction; Comorbid analysis; Machine learning; Healthcare AI; Personalized diagnostics
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Srisudha Garugu, Deva Harsha Sai Nangunuri, R. Srujana and Sahil Srivastava. Comorbid systematic health analyzer: A comprehensive AI-driven diagnostic tool for predicting diabetes and comorbid conditions. International Journal of Science and Research Archive, 2025, 14(01), 1252–1263. Article DOI: https://doi.org/10.30574/ijsra.2025.14.1.0183.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
