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.

ISSN

1546-2218

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

85102519856

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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