Predicting Chronic Diseases and Measuring Drug Effectiveness in Diabetes using Machine Learning and Statistical Methods to aid Clinical Decision Making

Abstract

 An increase in digitalization of patient health records has generated extensive data within the healthcare sector, yet the complexity and fragmentation of this data have hindered effective utilization for improved patient outcomes. This study examines the use of artificially intelligent systems (AI), the use of machine learning (ML), and big data analysis for medical purposes, with a focus on regulating and prediction of chronic illnesses. Transformation brought by AI and ML in diagnostics, patient care, and predictive analytics, emphasizing the significant role these technologies play in enhancing decision-making, improving data quality, reducing healthcare costs, and facilitating early disease detection and treatment is also discussed. Through a comprehensive review of various studies, the effectiveness of different ML models in clinical care, specifically in the context of chronic diseases like diabetes, cardiovascular diseases, and chronic kidney disease were examined. Moreover it analyze the challenges, of lack of standardized evaluation metrics and data governance in healthcare applications and highlight the potential of ML to revolutionize healthcare through improved diagnostic accuracy, personalized treatment plans, and better disease management strategies.