https://github.com/princeegy/heart-disease-prediction

A tool for predicting Heart Disease probability based on ML model
https://github.com/princeegy/heart-disease-prediction

heartdisease heartdisease-prediction machine-learning python streamlit xgboost

Last synced: 3 months ago
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A tool for predicting Heart Disease probability based on ML model

• Host: GitHub
• URL: https://github.com/princeegy/heart-disease-prediction
• Owner: PrinceEGY
• License: mit
• Created: 2022-10-03T23:39:09.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-17T22:12:56.000Z (over 2 years ago)
• Last Synced: 2024-12-30T19:39:42.291Z (5 months ago)
• Topics: heartdisease, heartdisease-prediction, machine-learning, python, streamlit, xgboost
• Language: Jupyter Notebook
• Homepage: https://princeegy-heart-disease-prediction-app-fgg77d.streamlitapp.com/
• Size: 10.8 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 
Heart Disease Prediction
  

  


Heart disease is the most common cause of death in the world, and approximately 1 in 5 people die from heart disease, this project can help predicting the probability of being affected by heart disease based on a well trained Machine Learning model  

  

## General Info  

- This project was created as a Mid-Project of Samsung Innovation Campus (SIC) training.

- The application construct is located in the app.py file. This file uses dataset from `Dataset folder` and the pretrained model from `Preprocessing & Modelling` folder

- **XGBoost** has got the best accuracy with **99%** accuracy on predicting people with no HeartDisease and **91%** accuracy on predicting people with Heart Disease  

  

![](images/app_img.png)  

  

## Team Members  

- [Ahmed Mohsen](https://github.com/PrinceEGY)

- [Hossam Galal](https://github.com/HossamGalal68)

- [Yomna Ramdan](https://github.com/Yomna-Ramadan)  

## Technologies  

- The app is fully written in `Python 3.10.1`, the user interface was created using `streamlit 1.13.0`

- Libraries used: `pandas`, `numpy`, `seaborn`, `matplotlib`, `sklearn`, `plotly`, `imblearn`  




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