Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.

Zhou, Mingjuan; Yao, Tianci; Li, Jian; Hui, Hui; Fan, Weimin; Guan, Yunfeng; Zhang, Aijun; Xu, Bufang

Zhou, Mingjuan; Yao, Tianci; Li, Jian; Hui, Hui; Fan, Weimin; Guan, Yunfeng; Zhang, Aijun; Xu, Bufang.

Afiliación

Zhou M; Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yao T; Shanghai National Engineering Research Center of Digital Television Co., Ltd., Shanghai, China.
Li J; Clinical Research Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Hui H; Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, China.
Fan W; Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Guan Y; School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Zhang A; Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xu B; Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Front Med (Lausanne) ; 9: 811890, 2022.

Article en En | MEDLINE | ID: mdl-36177329

Palabras clave

artificial intelligence; extreme gradient boosting (XGBoost); lifestyles; machine learning; semen quality

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Med (Lausanne) Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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