Using explainable artificial intelligence to identify patient-specific breast cancer subtypes
(1) Pembroke Pines Charter High School, (2) Knight Foundation School of Computing and Information Sciences Florida International University
https://doi.org/10.59720/23-145![Cover photo for Using explainable artificial intelligence to identify patient-specific breast cancer subtypes](https://emerginginvestigators.org/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBZzhRIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--329e1f4c39ec39a9a57c46a8a8acd41eb2682e19/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJY0c1bkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--33b2b080106a274a4ca568f8742d366d42f20c14/Feature%20and%20Homepage%20image.png)
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.
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