Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning for Healthcare: A Bibliometric Study of Contributions from Africa

Version 1 : Received: 30 January 2023 / Approved: 1 February 2023 / Online: 1 February 2023 (10:57:26 CET)
Version 2 : Received: 28 April 2023 / Approved: 3 May 2023 / Online: 3 May 2023 (14:43:44 CEST)

How to cite: Turki, H.; Pouris, A.; Ifeanyichukwu, F.M.; Namayega, C.; Hadj Taieb, M.A.; Adedayo, S.A.; Fourie, C.; Currin, C.B.; Asiedu, M.N.; Tonja, A.L.; Owodunni, A.T.; Dere, A.; Emezue, C.C.; Muhammad, S.H.; Isa, M.M.; Ben Aouicha, M. Machine Learning for Healthcare: A Bibliometric Study of Contributions from Africa. Preprints 2023, 2023020010. https://doi.org/10.20944/preprints202302.0010.v1 Turki, H.; Pouris, A.; Ifeanyichukwu, F.M.; Namayega, C.; Hadj Taieb, M.A.; Adedayo, S.A.; Fourie, C.; Currin, C.B.; Asiedu, M.N.; Tonja, A.L.; Owodunni, A.T.; Dere, A.; Emezue, C.C.; Muhammad, S.H.; Isa, M.M.; Ben Aouicha, M. Machine Learning for Healthcare: A Bibliometric Study of Contributions from Africa. Preprints 2023, 2023020010. https://doi.org/10.20944/preprints202302.0010.v1

Abstract

Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.

Keywords

Machine learning; Scientometrics; Africa; Research community; Open science; Health informatics

Subject

Social Sciences, Library and Information Sciences

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