Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa

Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa

Abstract Aim HIV prevention measures in sub-Saharan Africa are still short of attaining the UNAIDS 90–90-90 fast track targets set in 2014. Identifying predictors for HIV status may facilitate targeted screening interventions that improve health care. We aimed at identifying HIV predictors as well a...

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Bibliographic Details
Main Authors: Charles K. Mutai, Patrick E. McSharry, Innocent Ngaruye, Edouard Musabanganji
Format: Article
Language:English
Published: BMC 2021-07-01
Series:BMC Medical Research Methodology
Subjects:
Socio-behavioral
Screening
High-risk
Predictors
XGBoost
Online Access:https://doi.org/10.1186/s12874-021-01346-2

Internet

https://doi.org/10.1186/s12874-021-01346-2

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