overview.
Built Predicare, an AI-powered healthcare assistant that supports doctors with symptom analysis, heart disease risk prediction, and AI-assisted clinical decision-making through a unified platform.
about.
Predicare combines multiple machine learning models with an interactive web interface to provide real-time risk assessment and AI-assisted doctor consultations. It helps healthcare professionals make more informed clinical decisions.
technical implementation.
ML Model Integration
Integrated multiple TensorFlow models for symptom analysis and risk prediction. Implemented model explainability using SHAP values to help doctors understand AI recommendations.
Unified Backend Architecture
Built a Flask backend that orchestrates multiple ML models, handles patient data processing, and serves predictions via REST APIs. Implemented caching for frequently accessed predictions.
key features.
- AI-powered symptom analysis
- Heart disease risk prediction
- Real-time clinical decision support
- Interactive doctor consultation interface
- Multi-modal data integration
- Explainable ML model outputs
screenshots.
AI-powered symptom analysis interface.
Heart disease risk prediction dashboard.
challenges & solutions.
Challenge: Making AI predictions interpretable for healthcare professionals
Solution: Implemented SHAP (SHAPley Additive exPlanations) to provide feature importance rankings for each prediction, helping doctors understand why the AI made a specific recommendation.
Challenge: Handling sensitive patient data securely
Solution: Implemented end-to-end encryption for patient data, role-based access controls, and audit logging. Ensured HIPAA compliance in data handling and storage.