Machine learning-based prediction of Sasang constitution types using comprehensive clinical information and identification of key features for diagnosis.

Park, Sa-Yoon; Park, Musun; Lee, Won-Yung; Lee, Choong-Yeol; Kim, Ji-Hwan; Lee, Siwoo; Kim, Chang-Eop

Park, Sa-Yoon; Park, Musun; Lee, Won-Yung; Lee, Choong-Yeol; Kim, Ji-Hwan; Lee, Siwoo; Kim, Chang-Eop.

Afiliação

Park SY; Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Republic of Korea.
Park M; Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Republic of Korea.
Lee WY; Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Republic of Korea.
Lee CY; Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Republic of Korea.
Kim JH; Department of Sasang Constitutional Medicine, Gil Hospital of Korean Medicine, Gachon University, Incheon, Republic of Korea.
Lee S; Mibyeong Research Center, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
Kim CE; Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Republic of Korea.

Integr Med Res ; 10(3): 100668, 2021 Sep.

Article em En | MEDLINE | ID: mdl-33665087

Palavras-chave

Diagnostic model; Extremely randomized trees; Feature importance; Machine learning; Sasang constitutional medicine

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Integr Med Res Ano de publicação: 2021 Tipo de documento: Article País de publicação: Holanda

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