A Reliable Multi-classifier Multi-objective Model for Predicting Recurrence in Triple Negative Breast Cancer

Xi Chen, Zhiguo Zhou, Kimberly Thomas, Michael Folkert, Nathan Kim, Asal Rahimi, Jing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recurrence is a significant prognostic factor in patients with triple negative breast cancer, and the ability to accurately predict it is essential for treatment optimization. Machine learning is a preferred strategy for recurrence prediction. Most current predictive models are built based on single classifier and trained through a single objective. However, since many classifiers are available, selecting an optimal model is challenging. On the other hand, a single objective may not be a good measure to guide model training. We proposed a new multi-classifier multi-objective (MCMO) recurrence predictive model. Specifically, new similarity-based sensitivity and specificity were defined and considered as the two objective functions simultaneously during training. Also the evidential reasoning (ER) approach was used for fusing the output of each classifier to obtain more reliable outcome. Using the proposed MCMO model, we achieved a predictive area under the receiver operating characteristic curve (AUC) of 0.9 with balanced sensitivity and specificity. Furthermore, MCMO outperformed all the individual classifiers, and yielded more reliable results than other commonly used optimization and fusion methods.

Original languageEnglish (US)
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2182-2185
Number of pages4
ISBN (Electronic)9781538613115
DOIs
StatePublished - Jul 2019
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: Jul 23 2019Jul 27 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
CountryGermany
CityBerlin
Period7/23/197/27/19

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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    Chen, X., Zhou, Z., Thomas, K., Folkert, M., Kim, N., Rahimi, A., & Wang, J. (2019). A Reliable Multi-classifier Multi-objective Model for Predicting Recurrence in Triple Negative Breast Cancer. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 2182-2185). [8857030] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8857030