https://github.com/batthulavinay/disease_prediction
https://github.com/batthulavinay/disease_prediction
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/batthulavinay/disease_prediction
- Owner: BatthulaVinay
- Created: 2025-03-26T14:29:21.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-26T14:34:12.000Z (3 months ago)
- Last Synced: 2025-03-26T15:42:14.884Z (3 months ago)
- Language: Jupyter Notebook
- Size: 376 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Disease Prediction Using Machine Learning
## Overview
This project utilizes machine learning algorithms to predict diseases based on patient data. By analyzing various health parameters, the model provides a probabilistic diagnosis, aiding in early detection and intervention.
## Features
- Data preprocessing and feature engineering
- Implementation of multiple machine learning models
- Performance evaluation using key metrics
- Visualizations for data distribution and model performance
## Dataset
- The dataset used is `Training_Disease_prediction.csv`.
- It consists of various health-related features and the target variable `prognosis`.
- Missing values are handled by removing empty columns.
## Machine Learning Models Used
- Support Vector Machine (SVM)
- Naïve Bayes (GaussianNB)
- Random Forest Classifier
## Installation & Usage
1. **Clone the repository:**
```bash
git clone https://github.com/your-repo/disease-prediction.git
cd disease-prediction
```
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
3. **Run the Jupyter Notebook:**
```bash
jupyter notebook
```
4. **Open and execute** the `Disease Prediction Using Machine Learning.ipynb` file.
## Results & Evaluation
- The model's accuracy was evaluated using accuracy score and confusion matrix.
- Data visualization was used to check dataset balance and feature distribution.
## Future Enhancements
- Integration with a web-based UI for real-time predictions.
- Deployment using Flask/Django.
- Expansion to cover more diseases with additional datasets.
## License
This project is open-source and available under the [MIT License/other].