Open Access

REVIEW

A Comprehensive Review on Medical Diagnosis Using Machine Learning

Kaustubh Arun Bhavsar¹, Ahed Abugabah², Jimmy Singla^1,*, Ahmad Ali AlZubi³, Ali Kashif Bashir⁴, Nikita⁵

1 Lovely Professional University, Jalandhar, 144411, India
2 College of Technical Innovation, Zayed University, Dubai, United Arab Emirates
3 Department of Computer Science, King Saud University, Saudi Arabia
4 Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, M15 6BH, UK
5 School of Computer Science and Engineering, Lovely Professional University, Jalandhar, 144411, India

* Corresponding Author: Jimmy Singla. Email:

(This article belongs to the Special Issue: Machine Learning for Data Analytics)

Computers, Materials & Continua 2021, 67(2), 1997-2014. https://doi.org/10.32604/cmc.2021.014943

Received 28 October 2020; Accepted 14 December 2020; Issue published 05 February 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 learning methods by medical disciplines. Based on our review, we present future research directions that could be used to conduct further research.

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