Open Access

REVIEW

Medical Diagnosis Using Machine Learning: A Statistical Review

Kaustubh Arun Bhavsar¹, Jimmy Singla¹, Yasser D. Al-Otaibi², Oh-Young Song^3,*, Yousaf Bin Zikria⁴, Ali Kashif Bashir⁵

1 Lovely Professional University, Jalandhar, 144411, India
2 Department of Information Systems, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Software Department, Sejong University, Seoul, 05006, Korea
4 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea
5 Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, M15 6BH, UK

* Corresponding Author: Oh-Young Song. Email:

(This article belongs to this Special Issue: Intelligent Decision Support Systems for Complex Healthcare Applications)

Computers, Materials & Continua 2021, 67(1), 107-125. https://doi.org/10.32604/cmc.2021.014604

Received 02 October 2020; Accepted 20 October 2020; Issue published 12 January 2021

Abstract

Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry out the review, six different databases are selected. Inclusion and exclusion criteria are employed to limit the research. Further, the eligible articles are classified depending on publication year, authors, type of articles, research objective, inputs and outputs, problem and research gaps, and findings and results. Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis. The findings of this study show the most used ML methods and the most common diseases that are focused on by researchers. It also shows the increase in use of machine learning for disease diagnosis over the years. These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.

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