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Diverse Disease Prediction Models: Predict and detect five different diseases including breast cancer, diabetes, heart disease, lung cancer, and Parkinson's disease.
Streamlit-Powered Web Interface: Utilizes Streamlit library for creating an intuitive and efficient web interface for seamless interaction with disease prediction models.
End-to-End Deployment: Fully operational and deployed on Streamlit Cloud, ensuring easy accessibility for users worldwide.
Logistic Regression: Binary classification algorithm used for diseases like breast cancer and diabetes prediction.
Decision Tree Classification: Tree-like structure for diseases like heart disease to analyze multiple contributing factors.
Random Forest Classification: Ensemble learning method beneficial for diseases like lung cancer.
Support Vector Machine (SVM): Powerful algorithm suitable for diseases like heart disease with complex decision boundaries.
See owner's GitHub repository for more information: https://github.com/Shyam165/The-Machine-Learning-Disease-Prediction-Web-App
See owner's GitHub repository for more information: https://github.com/Shyam165/The-Machine-Learning-Disease-Prediction-Web-App
This application was not uploaded by the author, but through their publicly available Github repository, https://github.com/Shyam165/The-Machine-Learning-Disease-Prediction-Web-App.
Licensed under MIT License
Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.
- Clinical Informatics