https://github.com/jiteshshelke/complete-health-diagnostic-hub
🩺 Complete Health Diagnostic Hub – A 🌐 web-based platform using 🤖 machine learning to predict potential health risks for ❤️ heart, 🩸 kidney, 🏥 liver, and 🩹 diabetes conditions.
https://github.com/jiteshshelke/complete-health-diagnostic-hub
artificial-intelligence data-science flask-application healthcare healthcareai kaggle-dataset logistic-regression machine-learning machine-learning-algorithms medicalai python
Last synced: 27 days ago
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🩺 Complete Health Diagnostic Hub – A 🌐 web-based platform using 🤖 machine learning to predict potential health risks for ❤️ heart, 🩸 kidney, 🏥 liver, and 🩹 diabetes conditions.
- Host: GitHub
- URL: https://github.com/jiteshshelke/complete-health-diagnostic-hub
- Owner: JiteshShelke
- License: mit
- Created: 2025-03-31T09:38:39.000Z (about 1 month ago)
- Default Branch: master
- Last Pushed: 2025-03-31T10:15:27.000Z (about 1 month ago)
- Last Synced: 2025-03-31T10:38:33.252Z (about 1 month ago)
- Topics: artificial-intelligence, data-science, flask-application, healthcare, healthcareai, kaggle-dataset, logistic-regression, machine-learning, machine-learning-algorithms, medicalai, python
- Language: HTML
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Complete Health Diagnostic Hub 🏥💡
Welcome to the **Complete Health Diagnostic Hub** 🏥, a machine-learning-based system designed to predict the likelihood of heart disease, kidney disease, liver disease, and diabetes using advanced classification algorithms. 🚀
## 🌟 Features
- ✅ Predicts **Heart Disease**, **Kidney Disease**, **Liver Disease**, and **Diabetes**.
- 🤖 Utilizes **Logistic Regression, Decision Tree, KNN, Random Forest, and SVM** models.
- 🔬 Data sourced from **Kaggle** for robust training.
- 📊 Feature selection and preprocessing for optimal accuracy.
- 🌐 Flask-based web application for user-friendly predictions.
## 🏠 Technologies Used
- **Python 3.10** 🐍
- **Flask** 🌍 (For web deployment)
- **Pandas, NumPy** 📊 (Data Processing)
- **Matplotlib, Seaborn** 📈 (Data Visualization)
- **Scikit-learn** 🤖 (Machine Learning)
- **Joblib, Pickle** 🔄 (Model Serialization)
## 🛠️ Setup & Installation
```bash
# Clone the repository
git clone https://github.com/JiteshShelke/Complete-Health-Diagnostic-Hub.git
cd Complete-Health-Diagnostic-Hub
# Create a virtual environment
python -m venv env
source env/bin/activate # On Windows: env\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
## 🚀 How to Run
```bash
# Run the Flask app
python app.py
# Open in your browser
http://127.0.0.1:5000/
```
## 💂️ Project Web App Images
Here are some screenshots of the web application:
## 📂 Dataset Information
The project utilizes four medical datasets from **Kaggle**:
1. **Heart Disease Dataset** ❤️ - [Download Here](https://www.kaggle.com/code/syedali110/heart-disease-detection/input)
2. **Kidney Disease Dataset** 🩸 - [Download Here](https://www.kaggle.com/code/niteshyadav3103/chronic-kidney-disease-prediction-98-accuracy/input?select=kidney_disease.csv)
3. **Liver Disease Dataset** 🏥 - [Download Here](https://www.kaggle.com/datasets/rabieelkharoua/predict-liver-disease-1700-records-dataset/data)
4. **Diabetes Dataset** 🧑⚕️ - [Download Here](https://www.kaggle.com/datasets/akshaydattatraykhare/diabetes-dataset)
## 🎯 Methodology
1. **Data Collection** 💞 - Datasets sourced from Kaggle.
2. **Data Preprocessing** 🛠️ - Handling missing values, encoding, and scaling.
3. **Feature Selection** 🔍 - Key attributes selected based on medical relevance.
4. **Model Training** 🤖 - Various ML models trained and evaluated.
5. **Evaluation Metrics** 📊 - Accuracy, Precision, Recall, and F1-score.
6. **Deployment** 🌐 - Model integrated into a Flask web app.
## 🌍 API Endpoints
| Method | Endpoint | Description |
|--------|---------|-------------|
| `POST` | `/predict/heart` | Predicts Heart Disease |
| `POST` | `/predict/kidney` | Predicts Kidney Disease |
| `POST` | `/predict/liver` | Predicts Liver Disease |
| `POST` | `/predict/diabetes` | Predicts Diabetes |
## 🤝 Contributing
Pull requests are welcome! For significant changes, please open an issue first to discuss what you would like to change.
## 🐝 License
This project is licensed under the **MIT License**.
## 💎 Contact
For queries, reach out to **Jitesh Shelke** 👨💻 on GitHub: [@JiteshShelke](https://github.com/JiteshShelke)
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🌟 _Empowering healthcare with AI!_ 🌟