Welcome to the Complete Health Diagnostic Hub π₯, a machine-learning-based system designed to predict the likelihood of heart disease, kidney disease, liver disease, and diabetes using advanced classification algorithms. π
- β Predicts Heart Disease, Kidney Disease, Liver Disease, and Diabetes.
- π€ Utilizes Logistic Regression, Decision Tree, KNN, Random Forest, and SVM models.
- π¬ Data sourced from Kaggle for robust training.
- π Feature selection and preprocessing for optimal accuracy.
- π Flask-based web application for user-friendly predictions.
- Python 3.10 π
- Flask π (For web deployment)
- Pandas, NumPy π (Data Processing)
- Matplotlib, Seaborn π (Data Visualization)
- Scikit-learn π€ (Machine Learning)
- Joblib, Pickle π (Model Serialization)
# 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# Run the Flask app
python app.py
# Open in your browser
http://127.0.0.1:5000/Here are some screenshots of the web application:
The project utilizes four medical datasets from Kaggle:
- Heart Disease Dataset β€οΈ - Download Here
- Kidney Disease Dataset π©Έ - Download Here
- Liver Disease Dataset π₯ - Download Here
- Diabetes Dataset π§ββοΈ - Download Here
- Data Collection π - Datasets sourced from Kaggle.
- Data Preprocessing π οΈ - Handling missing values, encoding, and scaling.
- Feature Selection π - Key attributes selected based on medical relevance.
- Model Training π€ - Various ML models trained and evaluated.
- Evaluation Metrics π - Accuracy, Precision, Recall, and F1-score.
- Deployment π - Model integrated into a Flask web app.
| 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 |
Pull requests are welcome! For significant changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License.
For queries, reach out to Jitesh Shelke π¨βπ» on GitHub: @JiteshShelke
π Empowering healthcare with AI! π




