Content area

Abstract

Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors. Finally, this survey provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions.

Details

Title
Machine learning in medical applications: A review of state-of-the-art methods
Author
Shehab, Mohammad 1 ; Abualigah, Laith 2 ; Shambour, Qusai 3 ; Abu-Hashem, Muhannad A 4 ; Mohd Khaled Yousef Shambour 5 ; Alsalibi, Ahmed Izzat 6 ; Gandomi, Amir H 7 

 Information Technology, The World Islamic Sciences and Education University. Amman, Jordan 
 Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan; School of Computer Sciences, Universiti Sains Malaysia, Pulau, Pinang, 11800, Malaysia 
 Department of Software Engineering, Al-Ahliyya Amman University, Amman, Jordan 
 Department of Geomatics, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah, Saudi Arabia 
 Department of Scientific Information and Services, Umm Al-Qura University, Mecca, Saudi Arabia 
 Information System College, Israa University, Gaza-Palestine, Jordan 
 Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia 
Publication year
2022
Publication date
Jun 2022
Publisher
Elsevier Limited
ISSN
00104825
e-ISSN
18790534
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2663108508
Copyright
©2022. Elsevier Ltd