https://github.com/aaa1928/heart-disease-ml-classifier
A PyTorch model with a 99.27% accuracy designed to predict the risk of heart disease based on a combination of symptoms, lifestyle factors, and medical history.
https://github.com/aaa1928/heart-disease-ml-classifier
classification healthcare heart-disease heart-disease-detection heart-disease-prediction machine-learning neural-network python pytorch tabular tabular-data
Last synced: 28 days ago
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A PyTorch model with a 99.27% accuracy designed to predict the risk of heart disease based on a combination of symptoms, lifestyle factors, and medical history.
- Host: GitHub
- URL: https://github.com/aaa1928/heart-disease-ml-classifier
- Owner: aAa1928
- License: mit
- Created: 2025-02-22T05:18:47.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-22T07:05:35.000Z (8 months ago)
- Last Synced: 2025-02-22T07:29:06.205Z (8 months ago)
- Topics: classification, healthcare, heart-disease, heart-disease-detection, heart-disease-prediction, machine-learning, neural-network, python, pytorch, tabular, tabular-data
- Language: Python
- Homepage:
- Size: 2.93 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# heart-disease-ml-classifier
A PyTorch model designed to predict the risk of heart disease based on a combination of symptoms, lifestyle factors, and medical history from 70,000+ data samples.
The model achieves approximately 99.27% accuracy on test data.
[Download the model here](heart_disease_classifier_model.pth)
[Kaggle Dataset link](https://www.kaggle.com/datasets/mahatiratusher/heart-disease-risk-prediction-dataset)
## Overview
This project uses PyTorch to build a neural network classifier for heart disease risk prediction. The model analyzes several medical predictor variables to determine if a patient is at risk of heart disease.
## Dataset
The dataset contains medical predictor variables from the heart_disease_risk_dataset_earlymed.csv file.
It contains 18 medical predictors of heart disease:
1. Chest Pain: Presence of chest pain (Yes/No)
2. Shortness of Breath: Difficulty breathing (Yes/No)
3. Fatigue: Feeling of tiredness (Yes/No)
4. Palpitations: Irregular heartbeat sensations (Yes/No)
5. Dizziness: Feeling lightheaded (Yes/No)
6. Swelling: Edema in extremities (Yes/No)
7. Pain Arms Jaw Back: Pain radiating to arms/jaw/back (Yes/No)
8. Cold Sweats Nausea: Presence of cold sweats or nausea (Yes/No)
9. High BP: High blood pressure diagnosis (Yes/No)
10. High Cholesterol: High cholesterol diagnosis (Yes/No)
11. Diabetes: Presence of diabetes (Yes/No)
12. Smoking: Current smoking status (Yes/No)
13. Obesity: Obesity status (Yes/No)
14. Sedentary Lifestyle: Physical inactivity (Yes/No)
15. Family History: Family history of heart disease (Yes/No)
16. Chronic Stress: Ongoing stress condition (Yes/No)
17. Gender: Patient's gender (Male/Female)
18. Age: Age of patient in years
Output variable:
- Risk: Risk of Heart Disease (low/high)
## Requirements
- Python 3.8+
- PyTorch
- pandas
- scikit-learn
- matplotlib
## Usage
1. Clone the repository
2. Create a virtual environment `py -m venv .venv` and activate it `.venv/Scripts/activate`
3. Install dependencies: `pip install -r requirements.txt`
4. Run the model: `python main.py`
## Model Architecture
- Input layer: 18 features
- Hidden layer 1: 64 neurons with ReLU activation
- Hidden layer 2: 28 neurons with ReLU activation
- Output layer: 2 neurons (Binary classification)
- Optimization: Adam optimizer with learning rate 0.005
- Loss function: Cross Entropy Loss
## Results
The model is trained for 1000 epochs and the training progress is visualized through a loss plot that is automatically generated and saved as 'loss_plot.png'.
The model achieves approximately 99.27% accuracy on the test set, with results being reproducible using a fixed random seed (392).
## Model Persistence
The trained model is saved to 'heart_disease_classifier_model.pth' for later use.
## License
[MIT License](LICENSE)