Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review.

Area of Science:

• Computer Science
• Information Science
• African Studies

Background:

• Machine learning (ML) models are widely applied across diverse fields, driving technological advancements globally.
• ML technologies are recognized for their potential to address significant challenges in Africa, including poverty, education, healthcare, food security, and climate change.

Purpose of the Study:

• To conduct a critical bibliometric analysis and literature survey of machine learning research with an African perspective.
• To visualize the current landscape and future trends of ML research and applications in Africa.

Main Methods:

• A bibliometric analysis of 2761 ML-related documents published between 1993 and 2021.
• Documents were sourced from the Science Citation Index EXPANDED, covering 54 African countries.
• Analysis included identifying trends, collaborations, and research output over three decades.

Main Results:

• The study analyzed 2761 ML documents, predominantly articles (89%), with substantial citation counts.
• Research output spans 903 journals over three decades, originating from 54 African nations.
• The analysis provides a visualization of ML research trends and collaborations within Africa.

Conclusions:

• The bibliometric analysis offers insights into the current state and future trajectory of machine learning research in Africa.
• Findings are expected to facilitate future collaborative research and knowledge exchange among African researchers.
• This study highlights the growing importance and application of ML in addressing continental challenges.