Full-stack ML system that predicts 8 diseases and supports user-driven retraining via FastAPI and MLflow
Go to projectSmart Health AI is a robust full-stack ML-powered application that enables users to predict the risk of eight major diseases based on simple clinical inputs. Designed with a modular FastAPI backend and a snappy React + TailwindCSS frontend, the system goes a step further by enabling user feedback loops and live retraining of models—making it an end-to-end example of real-world MLOps.
Each disease has its own prediction pipeline built using scikit-learn, with MLflow tracking model metrics and versions. Users can submit new data post-prediction, and an endpoint is available for on-demand retraining, keeping the models continually updated.
Predicts 8 Diseases with ML Pipelines
Supports risk prediction for Anemia, Cardiovascular Disease, Heart Disease, Hepatitis C, Liver Disease, Lung Cancer, Stroke, and Thyroid Disease.
Modular ML Pipelines Per Disease
Each disease prediction is powered by its own standalone scikit-learn pipeline, allowing easy updates, experimentation, and isolated debugging—ensuring better maintainability and control.
MLflow-Powered MLOps
Model training runs, metrics, and artifacts are versioned and logged with MLflow.
FastAPI Backend & Docker Deployment
Built for production with FastAPI and Docker Compose for quick orchestration of backend + frontend.
Frontend with React + Vite + Tailwind
Sleek, responsive UI lets users input clinical data and view real-time predictions.
Built by Rohit Kshirsagar, Parth Lhase & Rishabh Kothari AI systems enthusiasts and founding engineers at ApexAI.
Check out more on GitHub or connect via LinkedIn.