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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...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Charles K. Mutai, Patrick E. McSharry, Innocent Ngaruye, Edouard Musabanganji
Format: Artigo
Sprache:Inglês
Veröffentlicht: BMC 2021-07-01
Schriftenreihe:BMC Medical Research Methodology
Schlagworte:
Socio-behavioral
Screening
High-risk
Predictors
XGBoost
Online-Zugang:https://doi.org/10.1186/s12874-021-01346-2
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