eprintid: 14149
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/41/49
datestamp: 2023-11-10 03:28:43
lastmod: 2023-11-10 03:28:43
status_changed: 2023-11-10 01:56:08
type: article
metadata_visibility: show
creators_name: Abdullah, T.A.A.
creators_name: Zahid, M.S.M.
creators_name: Ali, W.
title: A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions
ispublished: pub
note: cited By 25
abstract: We have witnessed the impact of ML in disease diagnosis, image recognition and classifi-cation, and many more related fields. Healthcare is a sensitive field related to people�s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Fur-thermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2021
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121535209&doi=10.3390%2fsym13122439&partnerID=40&md5=c8beba4fbe54517efeeb8d15d238234b
id_number: 10.3390/sym13122439
full_text_status: none
publication: Symmetry
volume: 13
number: 12
refereed: TRUE
issn: 20738994
citation:   Abdullah, T.A.A. and Zahid, M.S.M. and Ali, W.  (2021) A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions.  Symmetry, 13 (12).   ISSN 20738994