Machine learning in medical applications: A review of state-of-the-art methods.

Shehab, M; Abualigah, L; Shambour, Q; Abu-Hashem, MA; Shambour, MKY; Alsalibi, AI; Gandomi, AH

Machine learning in medical applications: A review of state-of-the-art methods.

Shehab, M Abualigah, L Shambour, Q Abu-Hashem, MA Shambour, MKY Alsalibi, AI Gandomi, AH

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Comput Biol Med, 2022, 145, pp. 105458
Issue Date:: 2022-06

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Field	Value	Language
dc.contributor.author	Shehab, M
dc.contributor.author	Abualigah, L
dc.contributor.author	Shambour, Q
dc.contributor.author	Abu-Hashem, MA
dc.contributor.author	Shambour, MKY
dc.contributor.author	Alsalibi, AI
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-03-28T03:31:00Z
dc.date.available	2022-03-24
dc.date.available	2023-03-28T03:31:00Z
dc.date.issued	2022-06
dc.identifier.citation	Comput Biol Med, 2022, 145, pp. 105458
dc.identifier.issn	0010-4825
dc.identifier.issn	1879-0534
dc.identifier.uri	http://hdl.handle.net/10453/168694
dc.description.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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Comput Biol Med
dc.relation.isbasedon	10.1016/j.compbiomed.2022.105458
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 11 Medical and Health Sciences
dc.subject.classification	Biomedical Engineering
dc.subject.mesh	Brain
dc.subject.mesh	Forecasting
dc.subject.mesh	Machine Learning
dc.subject.mesh	Reproducibility of Results
dc.subject.mesh	Brain
dc.subject.mesh	Reproducibility of Results
dc.subject.mesh	Forecasting
dc.subject.mesh	Machine Learning
dc.subject.mesh	Brain
dc.subject.mesh	Forecasting
dc.subject.mesh	Machine Learning
dc.subject.mesh	Reproducibility of Results
dc.title	Machine learning in medical applications: A review of state-of-the-art methods.
dc.type	Journal Article
utslib.citation.volume	145
utslib.location.activity	United States
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-06-01T00:00:00+1000Z
dc.date.updated	2023-03-28T03:30:58Z
pubs.publication-status	Published
pubs.volume	145

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.

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http://hdl.handle.net/10453/168694