Cytokine profiles as predictors of HIV incidence using machine learning survival models and statistical interpretable techniques

Cytokine profiles as predictors of HIV incidence using machine learning survival models and statistical interpretable techniques

Abstract HIV remains a critical global health issue, with an estimated 39.9 million people living with the virus worldwide by the end of 2023 (according to WHO). Although the epidemic’s impact varies significantly across regions, Africa remains the most affected. In the past decade, considerable eff...

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Bibliographic Details
Main Authors: Sarah Ogutu, Mohanad Mohammed, Henry Mwambi
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:Scientific Reports
Subjects:
Cytokine profiles
HIV incidence
Random survival forest
Survival support vector machine
SHAP values
C-index
Online Access:https://doi.org/10.1038/s41598-024-81510-y
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https://doi.org/10.1038/s41598-024-81510-y

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