STI/HIV risk prediction model development—A novel use of public data to forecast STIs/HIV risk for men who have sex with men

STI/HIV risk prediction model development—A novel use of public data to forecast STIs/HIV risk for men who have sex with men

A novel automatic framework is proposed for global sexually transmissible infections (STIs) and HIV risk prediction. Four machine learning methods, namely, Gradient Boosting Machine (GBM), Random Forest (RF), XG Boost, and Ensemble learning GBM-RF-XG Boost are applied and evaluated on the Demographi...

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Bibliographic Details
Main Authors: Xiaopeng Ji, Zhaohui Tang, Sonya R. Osborne, Thi Phuoc Van Nguyen, Amy B. Mullens, Judith A. Dean, Yan Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Public Health
Subjects:
human immunodeficiency virus
sexually transmissible infections
artificial intelligence
machine learning
risk prediction
Online Access:https://www.frontiersin.org/articles/10.3389/fpubh.2024.1511689/full
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