Subgroup Anomaly Detection using High-confidence Rules: Application to Healthcare Data

Juan L. Domínguez-Olmedo, Jacinto Mata, Victoria Pachón, Manuel Maña

2019

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.

Paper Citation

in Harvard Style

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) - Volume 5: HEALTHINF; ISBN 978-989-758-353-7, SciTePress, pages 431-435. DOI: 10.5220/0007555104310435

in Bibtex Style

@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) - Volume 5: HEALTHINF},
year={2019},
pages={431-435},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007555104310435},
isbn={978-989-758-353-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 5: HEALTHINF
TI - Subgroup Anomaly Detection using High-confidence Rules: Application to Healthcare Data
SN - 978-989-758-353-7
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