Download PDFOpen PDF in browserArtificial neural networks for prediction of final height in children with growth hormone deficiency6 pages•Published: June 4, 2018AbstractMathematical models predicting final height (FH) and its standard deviation score (SDS) for children with growth hormone deficiency is an important tool for clinicians to manage treatment process. Previously developed models do not have enough accuracy or not good enough for practical use. We used 5 binary and 7 continuous predictors available at the time of diagnosis and start of therapy and developed multiple linear regression (MLR) models and artificial neural networks (ANN). The sample included 121 patients of Endocrinology Research Center (Moscow, Russia) who were under observation in 1978-2016 and reached the final height. All of them received growth hormone replacement therapy at least for 3 years. MLR models had poor quality. The best ANN predicting FH has RMSE 4.8 cm and explains 71.3% of variance, and 10 predictors are used. The best ANN for predicting FH SDS ex- plains 50% of variance and has RMSE 0.749 SDS, and 12 predictors are used. It seems promising to increase the sample and improve the ANN models.Keyphrases: artificial neural network, children, final height, growth hormone deficiency, prediction, regression In: Oleg S. Pianykh, Alexey Neznanov, Sergei Kuznetsov, Jaume Baixeries and Svetla Boytcheva (editors). WDAM-2017. Workshop on Data Analysis in Medicine, vol 6, pages 83-88.
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