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Authors: Juan L. Domínguez-Olmedo ; Jacinto Mata ; Victoria Pachón and Manuel Maña

Affiliation: Escuela Técnica Superior de Ingeniería, University of Huelva, Huelva and Spain

Keyword(s): Anomaly Detection, Rules Discovery, Breast Cancer.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Health Information Systems ; Pattern Recognition and Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: In real datasets it often occurs that some cases behave differently from the majority. Such outliers may be caused by errors, or may have differential characteristics. It is very important to detect anomalous cases, which may negatively affect the analysis from the data, or bring valuable information. This paper describes an algorithm to address the task of automatically detect subgroups and the possible anomalies with respect to those subgroups. By the use of high-confidence rules, the algorithm determines those cases that satisfy a rule, and the cases discordant with that rule. We have applied this method to a dataset regarding information about breast cancer patients. The resulting subgroups and the corresponding outliers have been presented in detail.

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Paper citation in several formats:
Domínguez-Olmedo, J.; Mata, J.; Pachón, V. and Maña, M. (2019). Subgroup Anomaly Detection using High-confidence Rules: Application to Healthcare Data. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - HEALTHINF; ISBN 978-989-758-353-7; ISSN 2184-4305, SciTePress, pages 431-435. DOI: 10.5220/0007555104310435

@conference{healthinf19,
author={Juan L. Domínguez{-}Olmedo. and Jacinto Mata. and Victoria Pachón. and Manuel Maña.},
title={Subgroup Anomaly Detection using High-confidence Rules: Application to Healthcare Data},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - HEALTHINF},
year={2019},
pages={431-435},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007555104310435},
isbn={978-989-758-353-7},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - HEALTHINF
TI - Subgroup Anomaly Detection using High-confidence Rules: Application to Healthcare Data
SN - 978-989-758-353-7
IS - 2184-4305
AU - Domínguez-Olmedo, J.
AU - Mata, J.
AU - Pachón, V.
AU - Maña, M.
PY - 2019
SP - 431
EP - 435
DO - 10.5220/0007555104310435
PB - SciTePress

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