/heart_disease_prediction

This repository contains the hearth disease prediction EDA and ML analysis.

Primary LanguagePython

Heart Disease Prediction using Machine Learning

Overview

This repository contains the code and resources for a project that aims to predict the presence of heart disease using machine learning techniques. The goal is to develop a model that can accurately predict heart disease based on patient data, potentially aiding in early diagnosis and prevention.

Project Structure

├── README.md          <-- This file
├── data               <-- Folder containing the heart disease dataset
│   ├── raw            <-- Raw, unprocessed dataset
│   └── processed      <-- Processed dataset, ready for use
├── notebooks          <-- Notebooks for data exploration, analysis, and modeling
│   ├── EDA.ipynb      <-- Exploratory Data Analysis
│   ├── preprocessing.ipynb    <-- Data preprocessing and cleaning
│   └── modeling.ipynb  <-- Model training and evaluation
├── src                <-- Python scripts for data preprocessing, modeling, and evaluation
│   ├── preprocess.py  <-- Functions for data preprocessing
│   ├── model.py       <-- Functions for model training and evaluation
│   └── utils.py       <-- Utility functions
├── requirements.txt   <-- List of required python libraries
└── models             <-- Folder to store trained model files

Dataset

The dataset used in this project is the Heart Disease UCI dataset, available from the UCI Machine Learning Repository. It contains 303 records of patients, each with 14 attributes, including:

  • Age
  • Sex
  • Chest pain type
  • Resting blood pressure
  • Serum cholesterol
  • Fasting blood sugar
  • Resting ECG results
  • Maximum heart rate achieved
  • Exercise induced angina
  • ST depression induced by exercise relative to rest
  • The slope of the peak exercise ST segment
  • Number of major vessels (0-3) colored by fluoroscopy
  • Thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
  • Diagnosis of heart disease (angiographic disease status)

Getting Started

  1. Clone this repository:

    git clone https://github.com/nandodevs/heart-disease-prediction

  2. Install the required libraries:

    pip install -r requirements.txt

  3. Explore the dataset and notebooks to understand the data and the modeling process.

  4. Run the notebooks to reproduce the results.

  5. Customize the code for your own experiments and model development.

Contributing

We welcome contributions to this project! Please feel free to submit pull requests or open issues for any improvements or suggestions.

License

This project is licensed under the MIT License. See the LICENSE file for details.