Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort

Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort

Abstract Background The early life risk factors of childhood obesity among preterm infants are unclear and little is known about the influence of the feeding practices. We aimed to identify early life risk factors for childhood overweight/obesity among preterm infants and to determine feeding practi...

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Bibliographic Details
Main Authors: Yuanqing Fu, Wanglong Gou, Wensheng Hu, Yingying Mao, Yunyi Tian, Xinxiu Liang, Yuhong Guan, Tao Huang, Kelei Li, Xiaofei Guo, Huijuan Liu, Duo Li, Ju-Sheng Zheng
Format: Article
Language:English
Published: BMC 2020-07-01
Series:BMC Medicine
Subjects:
Preterm infants
Childhood obesity
Early life risk factors
Machine learning
Online Access:http://link.springer.com/article/10.1186/s12916-020-01642-6

Internet

http://link.springer.com/article/10.1186/s12916-020-01642-6

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