Comorbid systematic health analyzer: A comprehensive AI-driven diagnostic tool for predicting diabetes and comorbid conditions

Garugu, Srisudha and Nangunuri, Deva Harsha Sai and R., Srujana and Srivastava, Sahil (2025) Comorbid systematic health analyzer: A comprehensive AI-driven diagnostic tool for predicting diabetes and comorbid conditions. International Journal of Science and Research Archive, 14 (1). pp. 1252-1263. ISSN 2582-8185

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.