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This research aims to identify key factors influencing lifetime HIV testing among adults using machine learning techniques.

March 19, 2026Open Access

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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

MMM MuhammadEast Tennessee State University BABless-me Oluwatobi AjaniEast Tennessee State University JSJamilu SaniFederal University Birnin Kebbi

Key Points

This research aims to identify key factors influencing lifetime HIV testing among adults using machine learning techniques.
Utilized population-based survey data from BRFSS 2023
Applied machine learning algorithms, particularly XGBoost
Incorporated explainable AI to enhance understanding of the factors influencing testing behaviors
Machine learning models effectively predicted HIV testing behaviors
Explainable AI provided insights into significant determinants
Findings support the development of targeted public health strategies

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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AI에게 질문

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View Full Paper

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

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Abstract

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