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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Diverse Disease Prediction Models: Predict and detect five different diseases including breast cancer, diabetes, heart disease, lung cancer, and Parkinson's disease.
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Streamlit-Powered Web Interface: Utilizes Streamlit library for creating an intuitive and efficient web interface for seamless interaction with disease prediction models.
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End-to-End Deployment: Fully operational and deployed on Streamlit Cloud, ensuring easy accessibility for users worldwide.
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Streamlit: The core technology used for web development, known for its simplicity and efficiency.
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Disease Prediction Models: Implementation and deployment of machine learning models for disease prediction tasks.
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End-to-End Deployment on Streamlit Cloud: Practical demonstration of making machine learning-driven applications accessible globally.
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Logistic Regression: Binary classification algorithm used for diseases like breast cancer and diabetes prediction.
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Decision Tree Classification: Tree-like structure for diseases like heart disease to analyze multiple contributing factors.
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Random Forest Classification: Ensemble learning method beneficial for diseases like lung cancer.
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Support Vector Machine (SVM): Powerful algorithm suitable for diseases like heart disease with complex decision boundaries.
The future scope of the project includes:
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Malaria Detection Module: Integration of deep learning algorithms for analyzing cell images for malaria parasites.
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Pneumonia Detection Module: Leveraging deep learning techniques for pneumonia detection from chest X-ray images.
- Shyam Singh (shyamsingh78790@gmail.com)
This project is licensed under the MIT License - see the LICENSE.md file for details.
- Inspiration drawn from various healthcare and machine learning projects.
- Gratitude to the open-source community for their invaluable contributions.
For inquiries, please contact the project lead at shyamsingh78790@gmail.com.