Transforming Type 2 Diabetes Management through Telemedicine, Data Mining and Environmental Insights

Abstract

     Diabetes mellitus is a prevalent chronic disease with significant implications for public health, counting an expanded chance of coronary heart malady, stroke, persistent kidney illness, misery, and useful inability. In India, the predominance of diabetes among grown-ups matured 20 a long time and more seasoned rose from 5.5% in 1990 to 7.7% in 2016.. Traditionally, diabetes management involves costly consultations and diagnostic tests, presenting challenges for timely diagnosis and treatment. There is evidence that type 2 diabetes mellitus (DM) may be impacted by additional environmental factors. The data on environmental factors of type 2 diabetes that have been found in databases have been compiled in this systematic review. In order to show how the environment and type 2 diabetes are related, it suggests a theoretical framework [15]. Advances in machine learning and telemedicine offer innovative solutions to address these challenges. Data mining, a crucial aspect of machine learning, facilitates the extraction of valuable insights from extensive datasets, enabling more efficient and effective diabetes management. This study explores a telemedicine-based system utilizing five classification techniques—Decision Tree, Naive Bayes, Support Vector Machine, and others—to predict Type-2 diabetes. By leveraging real-time data analysis, the system aims to enhance early diagnosis and management of Type-2 diabetes, potentially preventing progression to critical conditions. The results evaluate the effectiveness of these models in a telemedicine context, identifying the best-performing model to assist healthcare professionals in making informed decisions for early intervention and improved patient outcomes[1][2][6][7].