Machine learning identifies girls with central precocious puberty based on multi-source data

Objective: The study aimed to develop simplified diagnostic models for identifying girls with central precocious puberty (CPP), without the expensive and cumbersome gonadotropin-releasing hormone (GnRH) stimulation test, which is the gold standard for CPP diagnosis.

Materials and Methods: Female patients who had secondary sexual characteristics before 8 years old and had taken a GnRH analog (GnRHa) stimulation test at a medical center in Guangzhou, China were enrolled. Data from clinical visiting, laboratory tests and medical image examinations were collected. We first extracted features from unstructured data such as clinical reports and medical images. Then, models based on each single-source data or multi-source data were developed with Extreme Gradient Boosting (XGBoost) classifier to classify patients as CPP or non-CPP.

Results: The best performance achieved an AUC of 0.88 and Youden index of 0.64 in the model based on multi-source data. The performance of single-source model...