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🌟 Healthful — AI-Powered Heart Disease Risk Prediction Web App

A modern full-stack HealthTech application that predicts heart disease risk using XGBoost, served through a FastAPI backend, and visualized via a beautifully designed Next.js + TypeScript frontend with glass-morphism UI and a heart-themed animated background.

💻 Next.js (Frontend) • ⚙️ FastAPI (Backend API) • 🤖 XGBoost (Machine Learning Model) 🎨 Tailwind CSS + Custom UI • 🧠 Real-time Inference

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📌 Table of Contents

• Overview
• Features
• Tech Stack
• AI Model <<<<<<< HEAD
• Project Structure =======

e2696ac341c869a1fe65d5842d7dd79f05d1fa68

• Future Improvements
• Disclaimer

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🩺 Overview

Healthful is an AI-driven web application that helps users understand whether they fall into a low, moderate, or high risk range for potential heart-related issues based on lifestyle and vitals data.

Users complete a guided multi-step form, and the model generates a prediction in real-time through a FastAPI backend.

This project showcases:

• Practical use of Machine Learning in HealthTech
• Modern UI/UX design with glass-morphism
• Full-stack integration between Next.js ↔ FastAPI ↔ ML model
• Real-world application of XGBoost for structured data

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✨ Features

🔹 AI Prediction (XGBoost)

• Trained on structured health & lifestyle metrics
• Evaluated using Accuracy, Precision, Recall, ROC-AUC
• Optimized for real-time prediction

🔹 FastAPI Backend

• Clean API endpoint for ML inference
• Handles preprocessing & validation
• Fast & production-ready

🔹 Next.js Frontend (TypeScript)

• Step-by-step guided input form

• Persistent values when navigating between steps

• Clean, modern design with:

  • Glass-morphism cards
  • Gradient buttons
  • Large health-themed icons
  • Heart-animated background video

🔹 Friendly Result Output

• Displays Low, Moderate, or High risk
• Offers helpful supportive guidance
• User-centered tone

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🧠 AI Model

Algorithm Used:

➡️ XGBoost Classifier — chosen for its strong performance on structured/tabular data.

Important Features Used

• Age
• BMI
• Smoking & Alcohol Intake
• Physical Activity
• Stress Level
• Hypertension / Diabetes / Hyperlipidemia
• Blood Pressure
• Blood Sugar
• Cholesterol

Evaluation Metrics:

• Accuracy
• Precision & Recall
• F1 Score
• ROC-AUC

The model balances sensitivity and specificity to ensure reliable predictions.

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⚙️ Tech Stack

Frontend

• Next.js 16 (App Router)
• TypeScript
• Tailwind CSS
• custom glass styles
• Framer Motion (optional animations)

Backend

• FastAPI
• Uvicorn
• Pydantic (data validation)

Machine Learning

• XGBoost
• scikit-learn
• pandas / numpy
• joblib (model saving)

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📁 Project Structure

📦 Healthful
├── backend/
│   ├── app/
│       ├── features_list.joblib
│       ├── imputer.joblib
│       ├── scaler.joblib
│       ├── xgb_clf.joblib
│       ├── requirements.txt
│       └── main.py
│
│
├── frontend/
│   ├── app/
│   │   ├── heart-check/
│   │       └── page.tsx        
│   │   
│   ├── public/
│   └── package.json
│
└── README.md

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🚀 Future Improvements

• Add user authentication
• Store user history securely
• Add charts & health analytics
• Deploy to cloud (Vercel + Railway)
• Add explainability (SHAP)
• Multi-language support

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⚠️ Disclaimer

This project is built for demonstration purposes only. It is not a medical device and should not be used for clinical diagnosis or treatment decisions.

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