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The "Machine Learning Disease Prediction Web App" is a transformative healthcare application designed to empower users with the ability to predict and detect various diseases using advanced machine learning models. It integrates data science and web development to create a user-friendly and informative platform.

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Use Cases Limitations Evidence Owner's Insight

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

Prototype

Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.


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  • Clinical Informatics

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C Community Discovery

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