Mapping Cost-Sensitive Learning for Imbalanced Medical Data: Research Trends and Applications

Imane Araf, Ali Idri, Ali Idri, Ikram Chairi

2023

Abstract

Incorporating Machine Learning (ML) in medicine has opened up new avenues for leveraging complex medical data to enhance patient outcomes and advance the field. However, the imbalanced nature of medical data poses a significant challenge, resulting in biased ML models that perform poorly on the minority class of interest. To address this issue, researchers have proposed various approaches, among which Cost-Sensitive Learning (CSL) stands out as a promising technique to improve the accuracy of ML models. To the best of our knowledge, this paper presents the first systematic mapping study on CSL for imbalanced medical data. To comprehensively investigate the scope of existing literature, papers published from January 2010 to December 2022 and sourced from five major digital libraries were thoroughly explored. A total of 173 papers were selected and analyzed according to three classification criteria: publication years, channels and sources; medical disciplines; and CSL approaches. This study provides a valuable resource for researchers seeking to explore the current state of research and advance the application of CSL for imbalanced data in medicine.

Paper Citation

in Harvard Style

Araf I., Idri A. and Chairi I. (2023). Mapping Cost-Sensitive Learning for Imbalanced Medical Data: Research Trends and Applications. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-671-2, SciTePress, pages 265-272. DOI: 10.5220/0012176000003598

in Bibtex Style

@conference{kdir23,
author={Imane Araf and Ali Idri and Ikram Chairi},
title={Mapping Cost-Sensitive Learning for Imbalanced Medical Data: Research Trends and Applications},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2023},
pages={265-272},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012176000003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Mapping Cost-Sensitive Learning for Imbalanced Medical Data: Research Trends and Applications
SN - 978-989-758-671-2
AU - Araf I.
AU - Idri A.
AU - Chairi I.
PY - 2023
SP - 265
EP - 272
DO - 10.5220/0012176000003598
PB - SciTePress