Explained Learning and Hyperparameter Optimization of Ensemble Estimator on the Bio-Psycho-Social Features of Children and Adolescents

Abstract

For decades, projects have been carried out in various countries to assess the developmental status of children and adolescents using anthropometry and specific kinesiological measurements. There is a need for the ability to evaluate this developmental status using a sufficiently simple method or a calculation to be applicable in practice. The most commonly used feature for this purpose is currently body mass index (BMI). From recent experience, this feature may cause problems if used indiscriminately in the developmental phase of life. Therefore, we aimed to find a more suitable feature set. We used data from Artos, the national program monitoring school children and adolescents in Slovenia. The data was analyzed using machine learning (ML) tools to find the most important features to predict a motor efficiency index (MEI), which has been shown to correlate strongly with a person’s health prospects. After data preparation and training a baseline model, a feature selection process was performed, which promoted some features as candidates to predict the motor efficiency index sufficiently. By implementing a hyperparameter optimization, we tuned the ML model to improve its generalization and present the feature interaction more elaborately. We show that besides the single feature’s importance, the features’ interaction should be considered. In the case of MEI, we find that the skin fold thicknesses can complement BMI and contribute to a better development status assessment of children and adolescents.

Keywords

• motor efficiency index
• greedy feature selection
• gradient boosting
• explainable artificial intelligence
• feature interaction
• generalized additive models