The Applications of Machine Learning Techniques in Medical Data Processing Based on Distributed Computing and the Internet of Things

dc.authoridHeidari, Arash/0000-0003-4279-8551
dc.authorwosidHeidari, Arash/AAK-9761-2021
dc.contributor.authorAminizadeh, Sarina
dc.contributor.authorHeidari, Arash
dc.contributor.authorToumaj, Shiva
dc.contributor.authorDarbandi, Mehdi
dc.contributor.authorNavimipour, Nima Jafari
dc.contributor.authorRezaei, Mahsa
dc.contributor.authorTalebi, Samira
dc.date.accessioned2023-10-19T15:11:41Z
dc.date.available2023-10-19T15:11:41Z
dc.date.issued2023
dc.department-temp[Aminizadeh, Sarina] Islamic Azad Univ Tabriz, Med Fac, Tabriz, Iran; [Heidari, Arash] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran; [Heidari, Arash] Halic Univ, Dept Software Engn, Istanbul, Turkiye; [Toumaj, Shiva] Urmia Univ Med Sci, Orumiyeh, Iran; [Darbandi, Mehdi] Eastern Mediterranean Univ, Dept Elect & Elect Engn, TR-99628 Gazimagusa, Turkiye; [Navimipour, Nima Jafari] Kadir Has Univ, Dept Comp Engn, Istanbul, Turkiye; [Navimipour, Nima Jafari] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan; [Rezaei, Mahsa] Tabriz Univ Med Sci, Fac Surg, Tabriz, Iran; [Talebi, Samira] Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX USA; [Azad, Poupak] Univ Manitoba, Dept Comp Sci, Winnipeg, MB, Canada; [Unal, Mehmet] Nisantasi Univ, Dept Comp Engn, Istanbul, Turkiyeen_US
dc.description.abstractMedical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models.en_US
dc.identifier.citation30
dc.identifier.doi10.1016/j.cmpb.2023.107745en_US
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.pmid37579550en_US
dc.identifier.scopus2-s2.0-85169129103en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2023.107745
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5170
dc.identifier.volume241en_US
dc.identifier.wosWOS:001055956100001en_US
dc.identifier.wosqualityQ1
dc.institutionauthorJafari Navimipour, Nima
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods and Programs in Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCovid-19 PatientsEn_Us
dc.subjectDeliveryEn_Us
dc.subjectNetworkEn_Us
dc.subjectDiseaseEn_Us
dc.subjectContextEn_Us
dc.subjectSystemEn_Us
dc.subjectImpactEn_Us
dc.subjectModelEn_Us
dc.subjectCovid-19 Patients
dc.subjectDelivery
dc.subjectNetwork
dc.subjectDisease
dc.subjectContext
dc.subjectMedical data processingen_US
dc.subjectSystem
dc.subjectHealthcare data analysisen_US
dc.subjectImpact
dc.subjectDeep learningen_US
dc.subjectModel
dc.subjectDistributed computingen_US
dc.titleThe Applications of Machine Learning Techniques in Medical Data Processing Based on Distributed Computing and the Internet of Thingsen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication0fb3c7a0-c005-4e5f-a9ae-bb163df2df8e
relation.isAuthorOfPublication.latestForDiscovery0fb3c7a0-c005-4e5f-a9ae-bb163df2df8e

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