Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis

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dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorAlhassan, Gloria Nnadwa
dc.contributor.authorZaidi, Abdelhamid
dc.contributor.authorOki, Olukayode Ayodele
dc.contributor.authorAwotunde, Joseph Bamidele
dc.contributor.authorOgbuju, Emeka
dc.contributor.authorAkintoye, Kayode A.
dc.date.accessioned2025-06-03T13:59:26Z
dc.date.available2025-06-03T13:59:26Z
dc.date.issued2024
dc.departmentSağlık Bilimleri Fakültesi
dc.description.abstractThis bibliometric research explores the global evolution of machine learning applications in medical and healthcare research for 3 decades (1994 to 2023). The study applies data mining techniques to a comprehensive dataset of published articles related to machine learning applications in the medical and healthcare sectors. The data extraction process includes the retrieval of relevant information from the source sources such as journals, books, and conference proceedings. An analysis of the extracted data is then conducted to identify the trends in the machine learning applications in medical and healthcare research. The Results revealed the publications published and indexed in the Scopus and PubMed database over the last 30 years. Bibliometric Analysis revealed that funding played a more significant role in publication productivity compared to collaboration (co-authorships), particularly at the country level. Hotspots analysis revealed three core research themes on MLHC research hence demonstrating the importance of machine learning applications to medical and healthcare research. Further, the study showed that the MLHC research landscape has largely focused on ML applications to tackle various issues ranging from chronic medical challenges (e.g., cardiological diseases) to patient data security. The findings of this research may be useful to policy makers and practitioners in the medical and healthcare sectors and to global research endeavours in the field. Future studies could include addressing issues such as growing ethical considerations, integration, and practical applications in wearable technology, IoT, and smart healthcare systems.
dc.identifier.doi10.1016/j.iswa.2024.200441
dc.identifier.issn2667-3053
dc.identifier.urihttps://hdl.handle.net/11363/9868
dc.identifier.volume24
dc.identifier.wos001328036300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.institutionauthorAlhassan, Gloria Nnadwa
dc.language.isoen
dc.publisherELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
dc.relation.ispartofINTELLIGENT SYSTEMS WITH APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMachine learning
dc.subjectHealthcare analytics
dc.subjectArtificial Intelligence
dc.subjectMedical research
dc.subjectIoT
dc.subjectAlgorithms
dc.subjectBibliometric analysis
dc.titleEvolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis
dc.typeArticle

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