A Survey on Intelligent Technologies for Precocious Puberty Analysis Using Multisource Healthcare Datasets

Precocious puberty represents a growing pediatric
endocrine challenge, marked by early emergence of secondary
sexual characteristics in children, with notable prevalence in urban populations influenced by environmental exposure,
nutritional transition, lifestyle alteration. Early breast
development before eight years of age in females remains a
principal clinical indicator, reflecting premature activation or dysregulation of sex hormone secretion independent of normal gonadotropin control. This paper presents a structured review of intelligent computational technologies applied to precocious puberty assessment using multisource healthcare datasets. Diverse data modalities, including hormonal profiles, imaging records, clinical measurements, electronic health records, are
examined with respect to their diagnostic contribution.
Contemporary machine learning frameworks, deep learning
architectures, statistical approaches are comparatively analyzed
to evaluate performance, robustness, interpretability in
endocrine decision support. The paper highlights that deep
learning models trained on heterogeneous hormone datasets
demonstrate superior predictive accuracy, enhanced feature
representation, improved diagnostic consistency when
contrasted with traditional techniques. A unique contribution of
this work lies in its integrative synthesis of data sources,
algorithmic strategies, clinical relevance within a unified
analytical perspective. The findings underscore the potential of
intelligent systems to support early detection, risk stratification,
personalized clinical management for precocious puberty,
offering guidance for future research development,
translational application in pediatric endocrinology. The
proposed method achieves 95% accuracy, 93% sensitivity, 94%
specificity, and 0.96 area under curve, demonstrating reliable,
balanced, and robust precocious puberty classification
performance.