A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Med...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 23; H. 9; S. 4178
Hauptverfasser:	An, Qi, Rahman, Saifur, Zhou, Jingwen, Kang, James Jin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 22.04.2023 MDPI
Schlagworte:	Accuracy Algorithms Analysis Artificial Intelligence Classification Clinical decision making Computational linguistics Data analysis Data mining Datasets Discriminant analysis Disease Efficiency Electronic health records Epidemiology Health care industry Health care reform Health Care Sector healthcare Language processing Machine Learning machine learning algorithms Medical advice systems Medical care Medical diagnosis Medical research Medicine, Experimental mobile health Natural language interfaces Neural networks Pattern recognition Public health Quality management Review supervised learning Telemedicine Unsupervised Machine Learning Germany unsupervised machine learning mobile health machine learning machine learning algorithms supervised learning healthcare
ISSN:	1424-8220, 1424-8220
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Abstract	Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).
AbstractList	Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets). Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).
Audience	Academic
Author	Rahman, Saifur Kang, James Jin An, Qi Zhou, Jingwen
AuthorAffiliation	2 Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia 1 School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC 3216, Australia
AuthorAffiliation_xml	– name: 1 School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC 3216, Australia – name: 2 Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia
Author_xml	– sequence: 1 givenname: Qi orcidid: 0000-0003-4869-1763 surname: An fullname: An, Qi – sequence: 2 givenname: Saifur orcidid: 0000-0001-8345-0952 surname: Rahman fullname: Rahman, Saifur – sequence: 3 givenname: Jingwen surname: Zhou fullname: Zhou, Jingwen – sequence: 4 givenname: James Jin orcidid: 0000-0002-0242-4187 surname: Kang fullname: Kang, James Jin
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37177382$$D View this record in MEDLINE/PubMed
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SubjectTerms	Accuracy Algorithms Analysis Artificial Intelligence Classification Clinical decision making Computational linguistics Data analysis Data mining Datasets Discriminant analysis Disease Efficiency Electronic health records Epidemiology Health care industry Health care reform Health Care Sector healthcare Language processing Machine Learning machine learning algorithms Medical advice systems Medical care Medical diagnosis Medical research Medicine, Experimental mobile health Natural language interfaces Neural networks Pattern recognition Public health Quality management Review supervised learning Telemedicine Unsupervised Machine Learning
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Title	A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges
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