@inproceedings{153fc8b846ba46038b162c8cce485293,
title = "Predicting Overall Survival in METABRIC Cohort Using Machine Learning",
abstract = "Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer that presents very high relapse and mortality. However, due to differences in the genetic architecture associated with TNBC, patients have different outcomes and respond differently to available treatments. In this study, we predicted the overall survival of TNBC patients in the METABRIC cohort employing supervised machine learning to identify important clinical and genetic features that are associated with better survival. We achieved a slightly higher Concordance index than the state of art and identified biological pathways related to the top genes considered important by our model.",
keywords = "Breast Cancer, Machine Learning",
author = "Afroz Banu and Rayyan Ahmed and Saleh Musleh and Zubair Shah and Mowafa Househ and Tanvir Alam",
note = "Publisher Copyright: {\textcopyright} 2023 The authors and IOS Press.; 21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023 ; Conference date: 01-07-2023 Through 03-07-2023",
year = "2023",
month = jun,
day = "29",
doi = "10.3233/SHTI230577",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "632--635",
editor = "John Mantas and Parisis Gallos and Emmanouil Zoulias and Arie Hasman and Househ, {Mowafa S.} and Martha Charalampidou and Andriana Magdalinou",
booktitle = "Healthcare Transformation with Informatics and Artificial Intelligence",
address = "Netherlands",
}