Data science applications for non-communicable disease prevention and control in Africa: a systematic review protocol
Year: 2025
Type: article
Abstract: AIM: This systematic review protocol describes the proposed method for evaluating evidence from original articles to understand the benefits, opportunities, and challenges of using data science for non-communicable disease (NCD) prevention and control in Africa. METHODS: Literature searches will be implemented in five scientific databases and one search engine using predefined keywords/ MeSH terms to identify relevant articles. At least two reviewers will independently screen, appraise, extract, and summarise data from eligible articles using predefined inclusion criteria. RESULTS: This systematic review will consider primary studies from articles that applied data science methods (such as artificial intelligence, machine learning, and risk or prognosis model algorithms, among others) to address NCD prevention and control exclusively among Indigenous Africans. CONCLUSION: Findings from this systematic review could serve as a roadmap to guide future initiatives to apply data science methods to revolutionise NCD prevention and control in Africa.
Authors: Akinkunmi Okekunle, Muideen Olaiya, Paul Olowoyo, Grace Ajuwon, Alison Kinengyere, Judit Kumuthini, Osahon Asowata, Onoja Akpa, Adekunle Fakunle, Shakuntala Baichoo, Folakemi Odedina, Yosr Hamdi, Charles Agyemang, Bruce Ovbiagele, Mayowa Owolabi
Institutions: Seoul National University, University of Ibadan, Monash University, Afe Babalola University, Makerere University, University of the Western Cape, University of Mauritius, Mayo Clinic in Florida, Mayo Clinic Comprehensive Cancer Center (Florida), Institut Pasteur de Tunis, Amsterdam Public Health, Amsterdam University Medical Centers, University of Amsterdam, University of California, San Francisco
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7107863805