SynapseCare: Machine Learning Based Health Risk Assessment and Personalized Feedback System

Abstract

 The global healthcare systems handle unprecedented challenges in managing chronic diseases and providing personalized care to diverse populations. Traditional reactive healthcare approaches often result in delayed interventions, increased costs, and suboptimal patient out- comes. This paper presents SynapseCare, an innovative machine learning-based platform that revolutionizes health risk assessment through predictive analytics and personalized feedback mechanisms. The system includes data collected from a number of sources, like wearable device metrics and electronic health records, lifestyle parameters, and demographic information to generate comprehensive health risk profiles. Advanced machine learning techniques, like deep learning models and ensemble methods, use patient data to predict potential health risks with high accuracy. The platform delivers personalized recommendations through an intuitive interface, empowering users to make medical choices in data. Experimental validation demonstrates significant improvements in early risk detection, with the system achieving 92% accuracy in cardiovascular risk prediction and 89% accuracy in diabetes risk assessment. SynapseCare indicates a change in views to proactive, data-driven healthcare that can reduce cost while increase results for patients by using early intervention and personalized care strategies.