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Authors: Andrea Campagner 1 ; Federico Cabitza 2 and Davide Ciucci 3

Affiliations: 1 Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano–Bicocca, viale Sarca 336 – 20126 Milano, Italy, Datareg, Via Limonta, 89 – 20092 Cinisello Balsamo, Italy ; 2 Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano–Bicocca, viale Sarca 336 – 20126 Milano, Italy, IRCCS Istituto Ortopedico Galeazzi, via Galeazzi 4 – 20161 Milano, Italy ; 3 Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano–Bicocca, viale Sarca 336 – 20126 Milano, Italy

ISBN: 978-989-758-353-7

Keyword(s): Machine Learning, Uncertainty, Three–Way Decision, Medicine, Data Analysis.

Abstract: Uncertainty is an intrinsic component of the clinical practice, which manifests itself in a variety of different forms. Despite the growing popularity of Machine Learning–based Decision Support Systems (ML-DSS) in the clinical domain, the effects of the uncertainty that is inherent in the medical data used to train and optimize these systems remain largely under–considered in the Machine Learning community, as well as in the health informatics one. A particularly common type of uncertainty arising in the clinical decision–making process is related to the ambiguity resulting from either lack of decisive information (lack of evidence) or excess of discordant information (lack of consensus). Both types of uncertainty create the opportunity for clinicians to abstain from making a clear–cut classification of the phenomenon under observation and consideration. In this work, we study a Machine Learning model endowed with the ability to directly work with both sources of imperfect information mentioned above. In order to investigate the possible trade–off between accuracy and uncertainty given by the possibility of abstention, we performed an evaluation of the considered model, against a variety of standard Machine Learning algorithms, on a real–world clinical classification problem. We report promising results in terms of commonly used performance metrics. (More)

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Paper citation in several formats:
Campagner, A.; Cabitza, F. and Ciucci, D. (2019). Exploring Medical Data Classification with Three-Way Decision Trees.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-353-7, pages 147-158. DOI: 10.5220/0007571001470158

@conference{healthinf19,
author={Andrea Campagner. and Federico Cabitza. and Davide Ciucci.},
title={Exploring Medical Data Classification with Three-Way Decision Trees},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2019},
pages={147-158},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007571001470158},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Exploring Medical Data Classification with Three-Way Decision Trees
SN - 978-989-758-353-7
AU - Campagner, A.
AU - Cabitza, F.
AU - Ciucci, D.
PY - 2019
SP - 147
EP - 158
DO - 10.5220/0007571001470158

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