Breast Cancer Analysis and Predictions

Saturday. November 21, 2020 - 1 min

Breast Cancer Analysis and Predictions

Breast cancer is the second leading cause of death from cancer in women, but outcomes have improved if caught and treated early. Mammogram screenings are an excellent tool in identifying a potential tumorous mass, even before symptoms may appear. While imaging technology has increased resolution and clarity, interpretation of the data and image are still exposed to likely false positive or false negative findings by breast imaging radiologists. Can predictive analytics be used to hone and improve cancerous tumor diagnosis? Data from the "Diagnostic Wisconsin Breast Cancer Database" was analyzed and ran through four predictive classifier models. This analysis shows that machines can diagnose breast cancer at a highly effective rate through machine learning, thus creating another tool in the arsenal of early detection and minimizing the damaging effects of the disease.

GitHub repository can be found here: GitHub
Final Report can be found here: Final Report
Presentation can be found here: Presentation

Doug Marcum

Data Science | Machine Learning | Storytelling

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