This study is a contribution for the improvement of healthcare in children and in society generally. Thisstudyaimstopredictchildren’sheightwhentheybecomeadults,also known as“target height”, to allow for a better growth assessment and more personalized healthcare. The existing literature describes some existing prediction methods, based on longitudinal population studies and statistical techniques, which with few information resources, are able to produce acceptable results. The challenge of this study is in using a new approach based on machine learning to forecast the target height for children and (eventually) improve the existing height prediction accuracy. The goals of the study were achieved. The extreme gradient boosting regression (XGB) and light gradient boosting machine regression (LightGBM) algorithms achieved considerably better results on the height prediction. The developed model can be usefully applied by paediatricians and other clinical professionals in growth assessment.

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Child height prediction,Growth assessment,Data mining,XGB-Extreme Gradient Boosting Regression,LGBM- Light Gradient Boosting Machine Regression,Child perzonalied medicine

• Computer and Information Sciences - Natural Sciences
• Physical Sciences - Natural Sciences
• Chemical Sciences - Natural Sciences
• Other Natural Sciences - Natural Sciences
• Civil Engineering - Engineering and Technology
• Chemical Engineering - Engineering and Technology
• Materials Engineering - Engineering and Technology