Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Aakash Suresh (1), Masrur Sobhan (2), Ananda Mondal (2)

(1) Pembroke Pines Charter High School, (2) Knight Foundation School of Computing and Information Sciences Florida International University

Jan 12, 2024

https://doi.org/10.59720/23-145

Cover photo for Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Here the authors sought to use explainable artificial intelligence (XAI) to classify breast cancer subtypes within datasets of diagnosed patients. Following three trials they achieved a 95% success rate using the XGBoost model.

Download Full Article as PDF

This article has been tagged with:

explainable ai breast cancer patient-specific biomarkers tree-based classifier biomarkers

Copyright © 2024 Suresh, Sobhan, and Mondal. All JEI articles are distributed under the attribution non-commercial, no derivative license. This means that anyone is free to share, copy and distribute an unaltered article for non-commercial purposes provided the original author and source are credited.