Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study.

ABSTRACT:

Background

Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis-gonadotropin-releasing hormone (GnRH)-stimulation test or GnRH analogue (GnRHa)-stimulation test-is expensive and makes patients uncomfortable due to the need for repeated blood sampling.

Objective

We aimed to combine multiple CPP-related features and construct machine learning models to predict response to the GnRHa-stimulation test.

Methods

In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm wa

SUBMITTER: Pan L 

PROVIDER: S-EPMC6390190 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature