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It focuses on how medical datasets are used to detect diseases and reveal patterns. The review highlights that few studies address machine learning to enhance healthcare data accuracy and efficiency, and evaluates supervised and unsupervised algorithms for time-series heart-rate transmission metrics.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-comprehensive-review-on-machine-learning-in-healthcare-industry-classification-restrictions-opportunities-and-challenges/290296/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-comprehensive-review-on-machine-learning-in-healthcare-industry-classification-restrictions-opportunities-and-challenges/290296.png","ImageObject",300,407,{"name":92,"@type":93},"Lucas Martin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the review focus on in healthcare machine learning?","Question",{"text":113,"@type":114},"It focuses on the limited number of studies that use machine learning algorithms to improve healthcare data accuracy and efficiency, especially for time-series healthcare metrics such as heart-rate transmission.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which types of machine learning algorithms are reviewed?",{"text":118,"@type":114},"The review covers both supervised and unsupervised machine learning algorithms and discusses their effectiveness in healthcare applications.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the review relate machine learning to time-series healthcare data?",{"text":122,"@type":114},"It demonstrates time-series tasks based on past values and discusses feasibility for both small and large datasets while considering accuracy and efficiency for heart-rate data transmission.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290296,1789641262,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Research outputs 2022 to 2026  \n5-1-2023  \nA comprehensive review on machine learning in healthcare  \nindustry: Classification, restrictions, opportunities and challenges  \nQi An  \nSaifur Rahman Jingwen Zhou  \nJames Jin Kang  \nEdith Cowan University  \nFollow this and additional works at: [https://ro.ecu.edu.au/ecuworks2022-2026](https://ro.ecu.edu.au/ecuworks2022-2026)  \n Part of the Computer Sciences Commons  \n10.3390/s23094178  \nAn, Q., Rahman, S., Zhou, J., & Kang, J. J. (2023) . A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges. Sensors, 23(9), 4178. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s23094178  \nThis Journal Article is posted at Research Online.  \n[https://ro.ecu.edu.au/ecuworks2022-2026/2367](https://ro.ecu.edu.au/ecuworks2022-2026/2367)  \n sensors   \nReview  \nA Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges  \nQi An 1, Saifur Rahman 1, Jingwen Zhou 1 and James Jin Kang 2, *  \nCitation: An, Q.; Rahman, S.; Zhou, J.; Kang, J.J. A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges. Sensors 2023, 23, 4178 . [https://doi.org/10.3390/s23094178](https://doi.org/10.3390/s23094178)  \nAcademic Editors: Wenyan Jia, Yi Gao, Zhi-Hong Mao and Mingui Sun  \nReceived: 7 March 2023  \nRevised: 16 April 2023  \nAccepted: 18 April 2023  \nPublished: 22 April 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC 3216, Australia  \n2 Computing and Security, School of Science, Edith Cowan University, Joondalup, WA 6027, Australia  \n* [Correspondence: james.kang@ecu.edu.au](Correspondence: james.kang@ecu.edu.au)  \nAbstract: 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) .  \nKeywords: machine learning; machine learning algorithms; healthcare; mobile health; supervised learning; unsupervised machine learning  \n1. Introduction  \nMachine learning is a mechanism that enables machines to learn automatically without explicit programming. The main area of machine learning is to use advanced algorithmsand statistical techniques to access the data and predict accuracy instead of a rule-based system. The dataset is a primary component of machine learning accuracy prediction. As a result, the data are more relevant and the prediction is more accurate. Machine learning has been used in different fields, such as finance, retail, and the healthcare industry [1] . The rising use of machine learning in health","cbCainpzrWNiX3QS","https://ap.wps.com/l/cbCainpzrWNiX3QS","pdf",1851241,22,"English","# Abstract\n# Keywords\n# 1. Introduction\n## Background and use cases\n## Dataset and prediction fundamentals\n## Applications in healthcare imaging and disease classification","[{\"question\":\"What problem does the review focus on in healthcare machine learning?\",\"answer\":\"It focuses on the limited number of studies that use machine learning algorithms to improve healthcare data accuracy and efficiency, especially for time-series healthcare metrics such as heart-rate transmission.\"},{\"question\":\"Which types of machine learning algorithms are reviewed?\",\"answer\":\"The review covers both supervised and unsupervised machine learning algorithms and discusses their effectiveness in healthcare applications.\"},{\"question\":\"How does the review relate machine learning to time-series healthcare data?\",\"answer\":\"It demonstrates time-series tasks based on past values and discusses feasibility for both small and large datasets while considering accuracy and efficiency for heart-rate data transmission.\"}]","A Comprehensive Review on Machine Learning in Healthcare Industry - Classification, Restrictions, Opportunities and Challenges | PDF",55]