Myopia Risk Predictor Inspiration With myopia cases rising due to increased screen time, I aimed to build a tool that predicts a user’s risk based on eye tests and lifestyle data.
What I Learned I gained experience in integrating ML with a responsive UI while optimizing both accuracy and speed.
How I Built It Using Python for data processing and model implementation, Flask as the backend framework to handle requests and responses, and a modern frontend built with technologies like HTML, CSS, and JavaScript, I developed a system that efficiently collects user inputs, processes eye test results and lifestyle data, and applies a predictive model to generate real-time myopia risk assessments. The backend ensures smooth communication between the user interface and the model, while the frontend provides an engaging and intuitive experience for users to interpret their results easily.
Challenges Faced Optimizing prediction accuracy, handling diverse lifestyle data, and designing an intuitive UI were key challenges.
Final Outcome The project successfully delivers a sleek, user-friendly experience, empowering users with data-driven insights for better eye health.
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