Exploring Machine Learning for Predicting Peripheral and Central Precocious Puberty Through Cross-Hospital Validation.

Journal: Studies in health technology and informatics
Published Date: Aug 7, 2025

Abstract

Precocious puberty, including Peripheral Precocious Puberty (PPP) and Central Precocious Puberty (CPP), presents diagnostic challenges in pediatric endocrinology, leading to delayed interventions. This study utilized machine learning models-Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGB)-to predict and differentiate between PPP and CPP using 12 clinical features extracted from electronic medical records (EMRs). Internal validation on TMUH data showed XGB achieving the highest sensitivity (0.88) and AUC (0.86). In external validation with WFH data, RF demonstrated superior generalizability, with a sensitivity of 0.91 and AUC of 0.89. These results highlight RF's robustness for cross-hospital implementation and the potential of machine learning to improve early diagnosis of precocious puberty.

Authors

  • Chun-Yen Cheng
    Ph.D. Program in Medical Biotechnology, Taipei Medical University, Taipei, Taiwan.
  • Yung-Chun Chang
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
  • Nguyen Quoc Khanh Le
    In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan. Electronic address: khanhlee@tmu.edu.tw.
  • Chao-Hsu Lin
    Department of Pediatrics, Hsinchu Mackay Memorial Hospital, Hsinchu, Taiwan.
  • Jia-Woei Hou
    Department of Pediatrics, Cathay General Hospital, Taipei, Taiwan.
  • Chen Yang
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Tzu-Hao Chang
    International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan.
  • Min-Huei Hsu
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.

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