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Leveraging Explainable AI to Identify Determinants of Lifetime HIV Testing Among Adults in Tennessee, United States: Evidence for Targeted Public Health Strategies From BRFSS 2023
0
Zitationen
4
Autoren
2026
Jahr
Abstract
ML algorithms, particularly XGBoost, provide a robust and interpretable framework for predicting HIV testing behaviors in population-based survey data. Integrating ML with explainable AI methods can improve surveillance, support targeted interventions, and inform data-driven public health strategies.
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