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Predictors of self-reported sexually transmitted infections (STIs) among men in 54 low and middle income countries (LMICs): a comparison of deep learning and classical machine learning algorithms

Crossref DOI link: https://doi.org/10.1186/s12879-026-13010-5

Published Online: 2026-03-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Melaku, Mequannent Sharew

Yohannes, Lamrot

Sharew, Berhanu

Aemro, Agazhe

Alhur, Anas Ali

Mengstie, Melaku Alelign

Mengesha, Alexander Takele
License Information

Text and Data Mining valid from 2026-03-12

Version of Record valid from 2026-04-24
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Article History

Received: 15 September 2025

Accepted: 2 March 2026

First Online: 12 March 2026

Declarations

:

: This study utilized publicly available DHS data for secondary analysis, so ethical approval and individual consent were not required. The DHS public-use datasets are released under DHS IRB-approved protocols that ensure respondents, households, and communities remain anonymous. Personal identifiers such as names and addresses are excluded, safeguarding participant privacy. The MEASURE DHS Program maintains strict confidentiality to protect all respondents.

: Not applicable.

: The authors declare no competing interests.

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