Conference proceeding
An agent-based approach to predicting lymph node metastasis status in breast cancer
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 1315-1319
09 Dec 2021
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%.
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29 Record Views
2 citations in Scopus
Details
- Title
- An agent-based approach to predicting lymph node metastasis status in breast cancer
- Creators
- Sean Grimes - Drexel UniversityMark D Zarella - Drexel UniversityFernando U Garcia - Reading HospitalDavid E Breen - Drexel University
- Publication Details
- 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 1315-1319
- Conference
- 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science; Pathology (and Laboratory Medicine)
- Scopus ID
- 2-s2.0-85125170126
- Other Identifier
- 991019174738104721