Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis
This 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...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305324001157 |
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| author | Samuel-Soma M. Ajibade Gloria Nnadwa Alhassan Abdelhamid Zaidi Olukayode Ayodele Oki Joseph Bamidele Awotunde Emeka Ogbuju Kayode A. Akintoye |
| author_facet | Samuel-Soma M. Ajibade Gloria Nnadwa Alhassan Abdelhamid Zaidi Olukayode Ayodele Oki Joseph Bamidele Awotunde Emeka Ogbuju Kayode A. Akintoye |
| author_sort | Samuel-Soma M. Ajibade |
| collection | DOAJ |
| description | This 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. |
| format | Article |
| id | doaj-art-43f45c466a8c4779b7f8f1d555ad68d2 |
| institution | OA Journals |
| issn | 2667-3053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-43f45c466a8c4779b7f8f1d555ad68d22025-08-20T01:59:39ZengElsevierIntelligent Systems with Applications2667-30532024-12-012420044110.1016/j.iswa.2024.200441Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysisSamuel-Soma M. Ajibade0Gloria Nnadwa Alhassan1Abdelhamid Zaidi2Olukayode Ayodele Oki3Joseph Bamidele Awotunde4Emeka Ogbuju5Kayode A. Akintoye6Department of Computer Engineering, Istanbul Commerce University, Istanbul, Turkey; Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya, Selangor 47500, Malaysia; Department of Computer Science, Miva Open University, Abuja, Nigeria; Corresponding author.Faculty of Health Science, Department of Nursing, Istanbul Gelisim University, Istanbul, Turkiye; Western Caspian University, Baku, AzerbaijanDepartment of Mathematics, College of Science, Qassim University, P.O. Box 6644, Buraydah 51452, Saudi ArabiaDepartment of Information Technology Walter Sisulu University, South AfricaDepartment of Computer Science, Faculty of Communication and Information Sciences, University of Ilorin, Ilorin, NigeriaDepartment of Computer Science, Miva Open University, Abuja, Nigeria; Department of Computer Science, Federal University, Lokoja, NigeriaDepartment of Computer Science, The Federal Polytechnic, Ado Ekiti, NigeriaThis 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.http://www.sciencedirect.com/science/article/pii/S2667305324001157Machine learningHealthcare analyticsArtificial IntelligenceMedical researchIoTAlgorithms |
| spellingShingle | Samuel-Soma M. Ajibade Gloria Nnadwa Alhassan Abdelhamid Zaidi Olukayode Ayodele Oki Joseph Bamidele Awotunde Emeka Ogbuju Kayode A. Akintoye Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis Intelligent Systems with Applications Machine learning Healthcare analytics Artificial Intelligence Medical research IoT Algorithms |
| title | Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis |
| title_full | Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis |
| title_fullStr | Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis |
| title_full_unstemmed | Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis |
| title_short | Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis |
| title_sort | evolution of machine learning applications in medical and healthcare analytics research a bibliometric analysis |
| topic | Machine learning Healthcare analytics Artificial Intelligence Medical research IoT Algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2667305324001157 |
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