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This study aims to identify current applications and gaps in using data science methods for controlling non-communicable diseases in Africa.

July 18, 2026Communications MedicineOpen Access

Harnessing data science to control non-communicable diseases in Africa: a systematic review and gap analysis

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This study aims to identify current applications and gaps in using data science methods for controlling non-communicable diseases in Africa.
Conducted a systematic review and gap analysis as registered in PROSPERO (CRD42023406237).
Machine learning outperformed traditional statistical methods for risk stratification in 80.8% of studies.
76.0% of data science techniques for NCD prevention and control remain in exploratory phases with limited applications.
Identified critical gaps in funding, capacity building, policy, and cross-cultural data sharing affecting NCD management.

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Systematic review explores data science applications to prevent non-communicable diseases in Africa, suggesting critical gaps and opportunities.

Okekunle et al. (2026) studied this question.

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Systematic review explores data science applications to prevent non-communicable diseases in Africa, suggesting critical gaps and opportunities.