https://github.com/onome-joseph/heart-attack-prediction
This project estimates the likelihood of a heart attack for a patient based on their health parameters.
https://github.com/onome-joseph/heart-attack-prediction
classification-model data-science flask-application healthcare machine-learning random-forest-classifier
Last synced: about 1 year ago
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This project estimates the likelihood of a heart attack for a patient based on their health parameters.
- Host: GitHub
- URL: https://github.com/onome-joseph/heart-attack-prediction
- Owner: Onome-Joseph
- License: mit
- Created: 2024-11-22T20:41:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-25T00:06:03.000Z (over 1 year ago)
- Last Synced: 2025-02-12T06:39:09.167Z (over 1 year ago)
- Topics: classification-model, data-science, flask-application, healthcare, machine-learning, random-forest-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 1.18 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Heart Attack Prediction Model
## Overview
This project implements a Heart Attack Prediction Machine Learning Model that estimates the likelihood of a heart attack for a patient based on their health parameters. The model uses the **Random Forest algorithm** and achieves very high accuracy, making it a reliable tool for medical analysis and decision-making.
## Applications
- Healthcare Providers: Aid in early diagnosis and risk assessment for heart disease.
- Hospitals: Prioritize patients based on their likelihood of a heart attack, optimizing emergency care.
- Insurance Companies: Assist in risk profiling for health insurance policies.
- Preventive Health Programs: Identify high-risk individuals for targeted interventions.
# Heart Attack Risk Prediction Flask App
This project is a Flask-based web application that predicts the risk of a heart attack based on user input. The prediction is powered by a machine learning model.
---
## Features
- Interactive form to input patient details.
- Machine learning-based prediction for heart attack risk.
- Easy-to-use interface with a clean design.
---
## Installation
1. **Clone the repository:**
```bash
git clone https://github.com/Onome-Joseph/Heart-Attack-Prediction.git
```
2. **Create a virtual environment (recommended):**
```bash
python -m venv venv
```
Activate the virtual environment:
- On Windows:
```bash
venv\Scripts\activate
```
- On macOS/Linux:
```bash
source venv/bin/activate
```
3. **Install required dependencies:**
```bash
python
!pip install Flask
!pip install numpy
!pip install scikit-learn
!pip install pandas
```
---
## Running the Application
1. **Ensure the `classifier.pkl` file is in the root directory.**
This file contains the trained machine learning model. If it's missing, the app will not work.
2. **Start the Flask server:**
```bash
python Heart_attack_FLASK.py
```
3. **Access the web application:**
Open your browser and go to:
```
http://127.0.0.1:5000/
```
4. **Fill in the form and get the prediction!**
---
## Project Structure
```
your-repository-name/
│
├── Heart_attack_FLASK.py # Flask application script
├── classifier.pkl # Pre-trained machine learning model
├── requirements.txt # List of Python dependencies
├── templates/
│ └── front.html # HTML template for the web app (plain design)
│ └──front2.html # HTML template for the web app (better design)
├── App preview # Layout of the frontend design (front2)
└── README.md # Project documentation
```
---
### Contributions
Contributions are welcome! Feel free to fork the repository, suggest improvements.