Machine learning outperformed logistic regression classification even with limit sample size: A model to predict pediatric HIV mortality and clinical progression to AIDS

Machine learning outperformed logistic regression classification even with limit sample size: A model to predict pediatric HIV mortality and clinical progression to AIDS

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Logistic regression (LR) is the most common prediction model in medicine. In recent years, supervised machine learning (ML) methods have gained popularity. However, there are many concerns about ML utility for small sample sizes. In this study, we aim to compare the performance of 7 algorithms in th...

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Bibliographic Details
Main Authors: Sara Domínguez-Rodríguez, Miquel Serna-Pascual, Andrea Oletto, Shaun Barnabas, Peter Zuidewind, Els Dobbels, Siva Danaviah, Osee Behuhuma, Maria Grazia Lain, Paula Vaz, Sheila Fernández-Luis, Tacilta Nhampossa, Elisa Lopez-Varela, Kennedy Otwombe, Afaaf Liberty, Avy Violari, Almoustapha Issiaka Maiga, Paolo Rossi, Carlo Giaquinto, Louise Kuhn, Pablo Rojo, Alfredo Tagarro
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2022-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565414/?tool=EBI

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565414/?tool=EBI

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