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

Sara DomínguezRodríguez | Zendy; Miquel SernaPascual | Zendy; Andrea Oletto | Zendy; Shaun Barnabas | Zendy; Peter Zuidewind | Zendy; Els Dobbels | Zendy; Siva Danaviah | Zendy; Osee Behuhuma | Zendy; Maria Grazia Lain | Zendy; Paula Vaz | Zendy; Sheila Martínez Fernández | Zendy; Tacilta Nhampossa | Zendy; Elisa LópezVarela | Zendy; Kennedy Otwombe | Zendy; Afaaf Liberty | Zendy; Avy Violari | Zendy; Almoustapha Issiaka Maïga | Zendy; Paolo Rossi | Zendy; Carlo Giaquinto | Zendy; Louise Kuhn | Zendy; Pablo Rojo | Zendy; Alfredo Tagarro | Zendy

Open Access

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

Author(s) -

Sara DomínguezRodríguez,

Miquel SernaPascual,

Andrea Oletto,

Shaun Barnabas,

Peter Zuidewind,

Els Dobbels,

Siva Danaviah,

Osee Behuhuma,

Maria Grazia Lain,

Paula Vaz,

Sheila Martínez Fernández,

Tacilta Nhampossa,

Elisa LópezVarela,

Kennedy Otwombe,

Afaaf Liberty,

Avy Violari,

Almoustapha Issiaka Maïga,

Paolo Rossi,

Carlo Giaquinto,

Louise Kuhn,

Pablo Rojo,

Alfredo Tagarro

Publication year - 2022

Publication title -

plos one

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.99

H-Index - 332

ISSN - 1932-6203

DOI - 10.1371/journal.pone.0276116

Subject(s) - machine learning , support vector machine , logistic regression , artificial intelligence , random forest , naive bayes classifier , confusion matrix , medicine , sample size determination , hyperparameter , statistics , computer science , mathematics

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