Utilizing Machine Learning Techniques for Predictive Modeling and Early Detection of Chronic Diseases

The increasing prevalence of chronic diseases necessitates innovative solutions for early prediction and management. This study develops a machine learning-based system for chronic disease prediction, leveraging real-time data inputs and advanced computational models. The system integrates predictive analytics with user-friendly visualization tools, enabling accurate and efficient assessments of health risks. Using a dataset encompassing key health indicators, such as age, medical history, lifestyle factors, and physiological metrics, the study implements algorithms including Random Forest, Support Vector Machines (SVM), and Neural Networks to identify patterns and predict disease outcomes. Embedded programming and a graphical user interface (GUI) were employed to enhance accessibility, allowing users to interact seamlessly with the system for real-time analysis. The proposed model demonstrates high accuracy, with a prediction rate exceeding 90% for chronic diseases like diabetes, cardiovascular conditions, and hypertension. These results highlight the system's potential for scalable applications in clinical decision support and personalized health management, emphasizing the transformative role of machine learning in modern healthcare.