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https://github.com/msaf9/heart-risk-assessment
This repository contains the code and documentation for a heart disease prediction model using machine learning techniques. The goal of this project is to build a model that can predict the presence of heart disease based on various patient attributes such as age, sex, cholesterol levels, and other medical indicators.
https://github.com/msaf9/heart-risk-assessment
machine-learning python
Last synced: 9 days ago
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This repository contains the code and documentation for a heart disease prediction model using machine learning techniques. The goal of this project is to build a model that can predict the presence of heart disease based on various patient attributes such as age, sex, cholesterol levels, and other medical indicators.
- Host: GitHub
- URL: https://github.com/msaf9/heart-risk-assessment
- Owner: msaf9
- License: mit
- Created: 2024-05-12T03:49:58.000Z (8 months ago)
- Default Branch: master
- Last Pushed: 2024-06-30T17:36:02.000Z (6 months ago)
- Last Synced: 2024-11-08T08:49:23.931Z (2 months ago)
- Topics: machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 512 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Heart Risk Assessment Model
This repository contains a machine learning project for predicting heart disease using patient data. The project involves data preprocessing, exploratory data analysis, model selection, training, evaluation, and deployment.
## Table of Contents
- [Introduction](#introduction)
- [Dataset](#dataset)
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Modeling](#modeling)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
## Introduction
Heart disease is one of the leading causes of death worldwide. Early prediction of heart disease can help in taking preventive measures and saving lives. This project aims to build a machine learning model to predict the presence of heart disease based on various patient attributes.
## Dataset
The dataset used in this project contains the following attributes:
1. **age**: Age in years
2. **sex**: Sex (1 = male; 0 = female)
3. **chest pain type**: Chest pain type (1-4)
4. **resting bp s**: Resting blood pressure in mm Hg
5. **cholesterol**: Serum cholesterol in mg/dl
6. **fasting blood sugar**: Fasting blood sugar (1 if > 120 mg/dl, 0 otherwise)
7. **resting ecg**: Resting electrocardiogram results (0-2)
8. **max heart rate**: Maximum heart rate achieved
9. **exercise angina**: Exercise induced angina (1 = yes; 0 = no)
10. **oldpeak**: ST depression induced by exercise relative to rest
11. **ST slope**: The slope of the peak exercise ST segment (0-2)
12. **target**: Presence of heart disease (1 = yes; 0 = no)
## Installation
To run this project, you will need Python and the following libraries:
- pandas
- numpy
- scikit-learn
- matplotlib
You can install the required libraries using pip:
```bash
pip install pandas numpy scikit-learn matplotlib
```
## Usage
1. Clone the repository:
```bash
git clone https://github.com/your-username/HeartDiseasePrediction.git
cd HeartDiseasePrediction
```
2. Run the Jupyter Notebook or Python script to see the data processing, model training, and evaluation:
```bash
jupyter notebook notebooks/heart_disease_prediction.ipynb
```
3. To preprocess data, train models, and make predictions, run:
```bash
python scripts/heart_disease_prediction.py
```
## Project Structure
```tree
HeartDiseasePrediction/
├── data/
│ └── heart_disease_data.csv
├── notebooks/
│ └── heart_disease_prediction.ipynb
├── scripts/
│ └── heart_disease_prediction.py
├── models/
│ └── trained_model.pkl
├── README.md
└── requirements.txt
```
data/: Contains the dataset.
notebooks/: Jupyter Notebook with detailed steps and explanations.
scripts/: Python scripts for data preprocessing, model training, and evaluation.
models/: Saved trained models.
requirements.txt: List of required libraries.
## Modeling
The following steps are involved in the modeling process:
1. Data Preprocessing
2. Exploratory Data Analysis (EDA)
3. Feature Selection
4. Model Training (Logistic Regression, Decision Tree, Random Forest, etc.)
5. Model Evaluation (Accuracy, Precision, Recall, F1-Score, ROC-AUC)
## Results
The final model achieved an accuracy of **`87.39%`** on the test set. Detailed evaluation metrics and plots are provided in the notebook and script.
## License
[MIT License](LICENSE)