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JMIR Med Inform. 2019 Feb 12;7(1):e11728. doi: 10.2196/11728.

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

Pan L#1, Liu G#1, Mao X#2, Li H1, Zhang J1, Liang H#1, Li X#2.

Author information

1
Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
2
Department of Genetics and Endocrinology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
#
Contributed equally

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 was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models.

RESULTS:

Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability.

CONCLUSIONS:

The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

KEYWORDS:

GnRHa-stimulation test; central precocious puberty; machine learning; prediction model

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