Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques.

Benítez-Andrades, José Alberto; García-Ordás, María Teresa; Álvarez-González, María; Leirós-Rodríguez, Raquel; López Rodríguez, Ana F

Benítez-Andrades, José Alberto; García-Ordás, María Teresa; Álvarez-González, María; Leirós-Rodríguez, Raquel; López Rodríguez, Ana F.

Afiliação

Benítez-Andrades JA; SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, Universidad de León, León, Spain.
García-Ordás MT; SECOMUCI Research Group, Escuela de Ingenierías Industrial e Informática, Universidad de León, León, Spain.
Álvarez-González M; Faculty of Health Sciences, Universidad de León, Ponferrada, Spain.
Leirós-Rodríguez R; SALBIS Research Group, Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain.
López Rodríguez AF; Faculty of Health Sciences, Universidad de León, Ponferrada, Spain.

Digit Health ; 8: 20552076221111289, 2022.

Article em En | MEDLINE | ID: mdl-35832475

Palavras-chave

Machine learning; obstetric labor complications; postpartum urinary incontinence; primary prevention

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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