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

Diabetes mellitus is a prevalent chronic disease with significant implications for public health, including an expanded chance of coronary heart malady, stroke, persistent kidney illness, misery, and useful inability. In India, the predominance of diabetes among grownups 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. Additionally, a comprehensive study was conducted to investigate the relationship between the incidence of type 2 diabetes mellitus (T2DM) and environmental exposure to arsenic in the form of air, water, and food pathways. The majority of the analyzed studies examined the levels of arsenic in water samples, with analyses of urine, blood, serum, and plasma samples coming next. Groundwater supplies may get contaminated by arsenic, especially in regions where arsenic deposits are naturally occurring or as a result of industrial activity. Additionally, various meals contain it, particularly rice, seafood, and poultry. Besides, it might be released into the environment by industrial processes such as coal combustion, smelting, and mining, which could lead to occupational exposure. There may be a genetic component to the association between arsenic exposure and the onset of diabetes. Ultimately, diabetes mellitus is enhanced by arsenic pollution through air, food, and drinking water. 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 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 bestperforming model to assist healthcare professionals in making informed decisions for early intervention and improved patient outcomes.