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Streamlit-based Cancer Detection System harnesses the power of machine learning algorithms to assist in early detection and diagnosis of various types of cancer. Leveraging the intuitive interface of Streamlit, users can seamlessly interact with the system, empowering healthcare professionals with an efficient tool for analyzing medical data.

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Use Cases Limitations Evidence Owner's Insight

Users can employ the Cancer Detection System app for a variety of purposes. They can explore trends and insights about cancer data using the Dashboard feature, enabling a deeper understanding of population dynamics across different states and years. Additionally, the app allows users to develop advanced machine learning models for cancer detection by selecting and training algorithms such as Support Vector Machines or Decision Trees in the Machine Learning section. Furthermore, users can automate the process of exploring cancer datasets through the Profiling feature, generating comprehensive reports and insights to facilitate decision-making in cancer research and treatment. Moreover, the app offers flexibility for tailored analyses and tasks based on specific needs, allowing users to input parameters or criteria in the User Input section.

See owner's GitHub repository for more information: https://github.com/StealStreet/Cancer-Detection-System

See owner's GitHub repository for more information: https://github.com/StealStreet/Cancer-Detection-System

This application was not uploaded by the author, but through their publicly available Github repository, https://github.com/StealStreet/Cancer-Detection-System.

Prototype

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


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  • Clinical Informatics

Owner

S Sk Sofiquee Fiaz

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