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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/9/4178 |
| _version_ | 1797601729535016960 |
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| author | Qi An Saifur Rahman Jingwen Zhou James Jin Kang |
| author_facet | Qi An Saifur Rahman Jingwen Zhou James Jin Kang |
| author_sort | Qi An |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-11T04:07:48Z |
| format | Article |
| id | doaj.art-462183be52544893998076eb37e3d840 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:07:48Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-462183be52544893998076eb37e3d8402023-11-17T23:40:47ZengMDPI AGSensors1424-82202023-04-01239417810.3390/s23094178A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and ChallengesQi An0Saifur Rahman1Jingwen Zhou2James Jin Kang3School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC 3216, AustraliaSchool of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC 3216, AustraliaSchool of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC 3216, AustraliaComputing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, AustraliaRecently, 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).https://www.mdpi.com/1424-8220/23/9/4178machine learningmachine learning algorithmshealthcaremobile healthsupervised learningunsupervised machine learning |
| spellingShingle | Qi An Saifur Rahman Jingwen Zhou James Jin Kang A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges Sensors machine learning machine learning algorithms healthcare mobile health supervised learning unsupervised machine learning |
| title | A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges |
| title_full | A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges |
| title_fullStr | A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges |
| title_full_unstemmed | A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges |
| title_short | A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges |
| title_sort | comprehensive review on machine learning in healthcare industry classification restrictions opportunities and challenges |
| topic | machine learning machine learning algorithms healthcare mobile health supervised learning unsupervised machine learning |
| url | https://www.mdpi.com/1424-8220/23/9/4178 |
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