Research Area:  Machine Learning

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.

Keywords:  
Medical Diagnosis
Machine Learning
Deep Learning

Author(s) Name:  Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali AlZubi, Ali Kashif Bashir, Nikita

Publisher name:  Computers, Materials and Continua (Tech Science Press)

DOI:  10.32604/cmc.2021.014943

Volume Information:  Volume 67, Issue 2, Page 1997-2014

Paper Link:   https://zuscholars.zu.ac.ae/works/4101/