单位：[1]Graduate School, Beijing University of Chinese Medicine, Beijing, China,[2]International Medical Services, China-Japan Friendship Hospital, Beijing, China,[3]Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China,[4]Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
Background and ObjectivesAs the worldwide secular trends are toward earlier puberty, identification of contributing factors for precocious puberty is critical. We aimed to identify and optimize contributing factors responsible for onset of precocious puberty via machine learning/deep learning algorithms in girls. MethodsA cross-sectional study was performed among girls aged 6-16 years from 26 schools in Beijing based on a cluster sampling method. Information was gleaned online via questionnaires. Machine/deep learning algorithms were performed using Python language (v3.7.6) on PyCharm platform. ResultsOf 11308 students enrolled, there are 5527 girls, and 408 of them had experienced precocious puberty. Training 13 machine learning algorithms revealed that gradient boosting machine (GBM) performed best in predicting precocious puberty. By comparison, six top factors including maternal age at menarche, paternal body mass index (BMI), waist-to-height ratio, maternal BMI, screen time, and physical activity were sufficient in prediction performance, with accuracy of 0.9530, precision of 0.9818, and area under the receiver operating characteristic curve (AUROC) of 0.7861. The performance of the top six factors was further validated by deep learning sequential model, with accuracy reaching 92.9%. ConclusionsWe identified six important factors from both parents and girls that can help predict the onset of precocious puberty among Chinese girls.
第一作者单位：[1]Graduate School, Beijing University of Chinese Medicine, Beijing, China,[3]Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China,
通讯作者：
通讯机构：[2]International Medical Services, China-Japan Friendship Hospital, Beijing, China,[3]Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China,
推荐引用方式(GB/T 7714)：
Pang Bo,Wang Qiong,Yang Min,et al.Identification and Optimization of Contributing Factors for Precocious Puberty by Machine/Deep Learning Methods in Chinese Girls[J].FRONTIERS in ENDOCRINOLOGY.2022,13:doi:10.3389/fendo.2022.892005.
APA：
Pang, Bo,Wang, Qiong,Yang, Min,Xue, Mei,Zhang, Yicheng...&Niu, Wenquan.(2022).Identification and Optimization of Contributing Factors for Precocious Puberty by Machine/Deep Learning Methods in Chinese Girls.FRONTIERS in ENDOCRINOLOGY,13,
MLA：
Pang, Bo,et al."Identification and Optimization of Contributing Factors for Precocious Puberty by Machine/Deep Learning Methods in Chinese Girls".FRONTIERS in ENDOCRINOLOGY 13.(2022)
