Document Type
Article
Source of Publication
Computers, Materials and Continua
Publication Date
1-1-2021
Abstract
The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine learning could assist the doctors in making decisions on time, and could also be used as a second opinion or supporting tool. This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases. We present the various machine learning algorithms used over the years to diagnose various diseases. The results of this study show the distribution of machine learningmethods by medical disciplines. Based on our review, we present future research directions that could be used to conduct further research.
DOI Link
ISSN
Publisher
Computers, Materials and Continua (Tech Science Press)
Volume
67
Issue
2
First Page
1997
Last Page
2014
Disciplines
Computer Sciences | Medicine and Health Sciences
Keywords
Diagnostic system, Healthcare applications, Machine learning, Medical diagnosis
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Bhavsar, Kaustubh Arun; Abugabah, Ahed; Singla, Jimmy; AlZubi, Ahmad Ali; Bashir, Ali Kashif; and Nikita, "A comprehensive review on medical diagnosis using machine learning" (2021). All Works. 4101.
https://zuscholars.zu.ac.ae/works/4101
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series