Cardio-ML - Cardiovascular Risk Prediction Platform

Cardio-ML is a full-stack healthcare project that estimates cardiovascular risk using patient health metrics and lifestyle data. The goal was to make machine learning predictions understandable, fast, and practical through a clean user experience.

Overview

The platform combines an interactive frontend with a model-serving backend:

• A web interface where users enter clinical and lifestyle inputs.
• A prediction service that processes features and returns disease risk.
• A clear results view that presents prediction confidence as a percentage score.

What I Built

• Designed a responsive data-entry flow for patient information.
• Integrated a trained ML model through a dedicated prediction API.
• Added result visualizations to make outcomes easier to interpret.
• Structured the app so frontend and backend can evolve independently.

Tech Stack

• Frontend: Next.js, React, Tailwind CSS, shadcn/ui, Framer Motion, Recharts
• Backend: Flask, Scikit-Learn, Pandas, NumPy
• ML Workflow: feature preparation, model training, and serialized inference pipeline

Key Implementation Details

• Handles clinical inputs such as blood pressure, cholesterol, glucose, and activity level.
• Applies preprocessing and derived features (including BMI) before inference.
• Returns both a binary prediction and a probability-based risk score for clarity.

Impact

Cardio-ML demonstrates how data science and product design can work together in healthcare contexts. Instead of exposing raw model output, the application focuses on clarity, trust, and user-friendly decision support.

Learnings

This project reinforced two critical principles:

• ML output is only useful when users can quickly understand it.
• A strict contract between UI and model API keeps iteration reliable and fast.