@inproceedings{d8d03e188fff4a34a014fd40cd02e869,
title = "An agent-based approach to predicting lymph node metastasis status in breast cancer",
abstract = "We present a flexible, multi-agent approach to predictive classification problems which uses simple, modular agents that interact and share information socially in an arena with a variable number of participants. Opinion aggregation is accomplished using a honey-bee-derived optimization algorithm that improves accuracy and reduces variance compared with existing weighted and unweighted voter mechanisms. Confidence metrics may be derived from the agent interactions. We apply our system to a data set of 483 de-identified breast cancer patients to predict node-positive or node-negative disease with over 78.5% accuracy in general. When eliminating low-confidence predictions, which leaves 79.5% of patients, classification accuracy improves to 84.5%.",
keywords = "breast cancer, classification, collective-intelligence, multi-agent, nomogram, prediction, swarm, wisdom-of-crowds",
author = "Sean Grimes and Zarella, {Mark D.} and Garcia, {Fernando U.} and Breen, {David E.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",
year = "2021",
doi = "10.1109/BIBM52615.2021.9669624",
language = "English (US)",
series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1315--1319",
editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, {Xiaohua Tony} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",
booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",
}