https://github.com/ali-hey-0/diabetes_prediction
A machine learning project for predicting the likelihood of diabetes in patients based on clinical features. This repository provides an end-to-end pipeline: from data loading and preprocessing to model training, evaluation, and prediction.
https://github.com/ali-hey-0/diabetes_prediction
diabetes-prediction machine-learning pipeline
Last synced: 20 days ago
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A machine learning project for predicting the likelihood of diabetes in patients based on clinical features. This repository provides an end-to-end pipeline: from data loading and preprocessing to model training, evaluation, and prediction.
- Host: GitHub
- URL: https://github.com/ali-hey-0/diabetes_prediction
- Owner: Ali-hey-0
- Created: 2025-09-02T15:10:07.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-09-02T15:12:18.000Z (about 1 month ago)
- Last Synced: 2025-09-02T17:21:43.972Z (about 1 month ago)
- Topics: diabetes-prediction, machine-learning, pipeline
- Language: Python
- Homepage:
- Size: 25.4 KB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Diabetes Prediction
A machine learning project for predicting the likelihood of diabetes in patients based on clinical features. This repository provides an end-to-end pipeline: from data loading and preprocessing to model training, evaluation, and prediction. The project aims to demonstrate the application of supervised learning techniques in health informatics for disease risk prediction.
## Table of Contents
- [Features](#features)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Usage](#usage)
- [Dataset](#dataset)
- [Modeling Approach](#modeling-approach)
- [Evaluation Metrics](#evaluation-metrics)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
- [References](#references)
---
## Features
- Data preprocessing and feature engineering
- Multiple machine learning algorithms (Logistic Regression, Random Forest, SVM, etc.)
- Model evaluation with metrics and visualization
- Prediction interface for new patient data
- Well-structured, modular codebase
---
## Project Structure
```
diabetes_prediction/
│
├── data/ # Raw and processed datasets
├── notebooks/ # Jupyter notebooks for EDA and modeling
├── src/ # Source code for preprocessing, training, and prediction
│ ├── data_preprocessing.py
│ ├── model_training.py
│ ├── model_evaluation.py
│ └── predict.py
├── models/ # Saved trained model files
├── requirements.txt # Dependencies
├── README.md # Project documentation
└── main.py # Command-line interface for running the pipeline
```
---
## Installation
1. **Clone the repository:**
```bash
git clone https://github.com/Ali-hey-0/diabetes_prediction.git
cd diabetes_prediction
```
2. **Install the required dependencies:**
```bash
pip install -r requirements.txt
```
Common libraries used:
- `pandas` for data manipulation
- `numpy` for numerical computations
- `scikit-learn` for modeling
- `matplotlib`/`seaborn` for visualization
- `joblib` for model serialization
---
## Usage
To run the full training and evaluation pipeline:
```bash
python main.py --train --evaluate
```
To predict diabetes for new patient data:
```bash
python main.py --predict --input data/new_patients.csv
```
Or use the Jupyter notebooks in `notebooks/` for interactive exploration.
---
## Dataset
The project uses the [Pima Indians Diabetes Database](https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database) or a similar clinical dataset with the following features:
- `Pregnancies`
- `Glucose`
- `BloodPressure`
- `SkinThickness`
- `Insulin`
- `BMI`
- `DiabetesPedigreeFunction`
- `Age`
- `Outcome` (target variable: 1 = diabetic, 0 = non-diabetic)
---
## Modeling Approach
- **Data preprocessing:** Handle missing values, outlier detection, and feature scaling.
- **Feature engineering:** Optionally create new features or select relevant features.
- **Model selection:** Try multiple classifiers (Logistic Regression, Random Forest, SVM, etc.).
- **Hyperparameter tuning:** Use grid search or cross-validation to optimize models.
- **Model evaluation:** Assess performance using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix.
---
## Evaluation Metrics
- **Accuracy:** Proportion of correct predictions.
- **Precision & Recall:** For imbalanced classes.
- **F1-Score:** Harmonic mean of precision and recall.
- **ROC-AUC:** Model's ability to distinguish between classes.
- **Confusion Matrix:** Visual assessment of predictions.
---
## Results
The best model achieved the following scores on the test set:
| Metric | Score |
| -------------- | -------- |
| Accuracy | 0.87 |
| Precision | 0.82 |
| Recall | 0.85 |
| F1-Score | 0.83 |
| ROC-AUC | 0.89 |
> *Note: These are example values. Actual results may vary based on data and model configuration.*
---
## Contributing
Contributions are welcome! Please open issues or pull requests for improvements or bug fixes.
1. Fork the repository
2. Create a new branch (`git checkout -b feature/my-feature`)
3. Commit your changes (`git commit -am 'Add new feature'`)
4. Push to the branch (`git push origin feature/my-feature`)
5. Open a pull request
---
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
## References
- [scikit-learn documentation](https://scikit-learn.org/)
- [Pima Indians Diabetes Database on Kaggle](https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database)
- [ML best practices](https://developers.google.com/machine-learning/guides/rules-of-ml)
---