Inspiration:
We were inspired by the idea that healthcare is often reactive rather than proactive. People usually seek help only when symptoms worsen. With the rise of wearables and affordable sensors, we felt the opportunity to build a system that gives every person a “Digital Health Twin” that mirrors their health in real time. Our motivation was to create a tool that provides early awareness, calm guidance, and simple explanations, without causing fear or panic.
Challenges I ran into: We noticed a major gap between daily health monitoring and actual clinical insights. Users can measure heart rate or blood pressure, but they do not know what it means, what to do next, or whether to worry. We wanted to bridge this gap by combining vitals, symptoms, and AI-generated medical explanations into one user-friendly system.
What I learned: We learned how lightweight ML models like RandomForest, Logistic Regression, and IsolationForest can be combined to detect early health patterns. We also gained experience in building modular Flask APIs, prompting AI models safely, designing dashboards, and securing data using hash chaining. This project helped us understand how technical design intersects with medical responsibility.
