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🩺 Complete Health Diagnostic Hub – A 🌐 web-based platform using πŸ€– machine learning to predict potential health risks for ❀️ heart, 🩸 kidney, πŸ₯ liver, and 🩹 diabetes conditions.

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Complete Health Diagnostic Hub πŸ₯πŸ’‘

Health Diagnostic

Welcome to the Complete Health Diagnostic Hub πŸ₯, a machine-learning-based system designed to predict the likelihood of heart disease, kidney disease, liver disease, and diabetes using advanced classification algorithms. πŸš€

🌟 Features

  • βœ… Predicts Heart Disease, Kidney Disease, Liver Disease, and Diabetes.
  • πŸ€– Utilizes Logistic Regression, Decision Tree, KNN, Random Forest, and SVM models.
  • πŸ”¬ Data sourced from Kaggle for robust training.
  • πŸ“Š 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 πŸ“Š (Data Processing)
  • Matplotlib, Seaborn πŸ“ˆ (Data Visualization)
  • Scikit-learn πŸ€– (Machine Learning)
  • Joblib, Pickle πŸ”„ (Model Serialization)

πŸ› οΈ Setup & Installation

# 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

πŸš€ How to Run

# Run the Flask app
python app.py

# Open in your browser
http://127.0.0.1:5000/

πŸ’‚οΈ Project Web App Images

Here are some screenshots of the web application:

Web App Home Web first Prediction Page Results Page

πŸ“‚ Dataset Information

The project utilizes four medical datasets from Kaggle:

  1. Heart Disease Dataset ❀️ - Download Here
  2. Kidney Disease Dataset 🩸 - Download Here
  3. Liver Disease Dataset πŸ₯ - Download Here
  4. Diabetes Dataset πŸ§‘β€βš•οΈ - Download Here

🎯 Methodology

  1. Data Collection πŸ’ž - Datasets sourced from Kaggle.
  2. Data Preprocessing πŸ› οΈ - Handling missing values, encoding, and scaling.
  3. Feature Selection πŸ” - Key attributes selected based on medical relevance.
  4. Model Training πŸ€– - Various ML models trained and evaluated.
  5. Evaluation Metrics πŸ“Š - 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.

πŸ’Ž Contact

For queries, reach out to Jitesh Shelke πŸ‘¨β€πŸ’» on GitHub: @JiteshShelke


🌟 Empowering healthcare with AI! 🌟

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🩺 Complete Health Diagnostic Hub – A 🌐 web-based platform using πŸ€– machine learning to predict potential health risks for ❀️ heart, 🩸 kidney, πŸ₯ liver, and 🩹 diabetes conditions.

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