Predicting Breast Cancer with Machine Learning
What sets the Curiosity Cup 2025, the global competition, apart is its unique format: students choose their own datasets and explore topics they’re truly passionate about.
In the Data Analysis category, first place was awarded to Team Machine Learning Dynamite from Anglia Ruskin University, UK. They also earned runner-up honors in the Data Presentation category. Their chosen topic came from the medical field, and it was no small challenge.
They tackled one of the most complex issues in healthcare: detecting invasive Ductal Carcinoma (IDC), the most common type of breast cancer, using histopathology image data.
Using SAS Viya Workbench for Learners and advanced machine learning techniques, they developed and validated two predictive models: a decision tree and a high-performance neural network, both demonstrating impressive classification accuracy.
Their methodology combined RGB color features, texture metrics, and statistical descriptors to build an automated diagnostic support tool. This tool has the potential to enhance clinical workflows and reduce diagnostic inconsistencies in real-world settings.
We proudly celebrate their innovation, analytical rigor, and commitment to meaningful impact.
Team was created by Students: Omoegbemwen Aigbe, Richard Malatesta, Bernadette Chan, Yazid Shuaibu, and their Faculty Advisor, Jason Hodgson
You can view their full paper as a PDF under the main headline of this article.
