💓 Heart Attack Risk Predictor

A machine learning-powered web application that predicts a patient's risk of having a heart attack based on key clinical parameters.

🧠 Project Overview

This application takes in medical inputs such as age, blood pressure, heart rate, and cardiac enzyme levels to estimate the likelihood of a heart attack using a trained Random Forest model. The web interface is built using Streamlit.

DEMO LINK

📁 Folder Structure

project/
├── app.py
├── model/
│ └── heart_model.pkl
├── assets/
│ ├── heart.png
│ └── icon.png

🧪 Features

• User-friendly web interface
• Real-time prediction of heart attack risk
• Model trained using:
  • Age
  • Gender
  • Heart rate
  • Systolic & Diastolic blood pressure
  • Blood sugar
  • CK-MB & Troponin levels

📊 Dataset

Sourced from Kaggle, the dataset includes clinical data points relevant to heart health.

🚀 Installation

1. Clone the repository

git clone https://github.com/Hasan-Uddin/HeartAttackPredictionUsingML-App.git
cd heart_attack_predictor

2. Install dependencies

pip install -r requirements.txt

3. Train the model

cd model
jupyter notebook train_model.ipynb

4. Run the Streamlit app

cd heart_attack_predictor
streamlit run app.py