In the field of machine learning, a considerable amount of research is involved in the interpretability of models and their decisions. The interpretability contradicts the model quality. Random Forests are among the best quality technologies of machine learning, but their operation is of “black box” character. Among the quantifiable approaches to the model interpretation, there are measures of association of predictors and response. In case of the Random Forests, this approach usually consists of calculating the model’s feature importances. Known methods, including the built-in one, are less suitable in settings with strong multicollinearity of features. Therefore, we propose an experimental approach to the feature selection task, a greedy forward feature selection method with least-trees-used criterion. It yields a set of most informative features that can be used in a machine learning (ML) training process with similar prediction quality as the original feature set. We verify the results of the proposed method on two known datasets, one with small feature multicollinearity and another with large feature multicollinearity. The proposed method also allows for a domain expert help with selecting among equally important features, which is known as the human-in-the-loop approach.

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.

In the era of ever-greater data production and collection, public health research is often limited by the scarcity of data. To improve this, we propose data sharing in the form of Data Spaces, which provide technical, business, and legal conditions for an easier and trustworthy data exchange for all the participants. The data must be described in a commonly understandable way, which can be assured by machine-readable ontologies. We compared the semantic interoperability technologies used in the European Data Spaces initiatives and adopted them in our use case of physical development in children and youth. We propose an ontology describing data from the Analysis of Children’s Development in Slovenia (ACDSi) study in the Resource Description Framework (RDF) format and a corresponding Next Generation Systems Interface-Linked Data (NGSI-LD) data model. For this purpose, we have developed a tool to generate an NGSI-LD data model using information from an ontology in RDF format. The tool builds on the declaration from the standard that the NGSI-LD information model follows the graph structure of RDF, so that such translation is feasible. The source RDF ontology is analyzed using the standardized SPARQL Protocol and RDF Query Language (SPARQL), specifically using Property Path queries. The NGSI-LD data model is generated from the definitions collected in the analysis. The translation has been verified on Smart Applications REFerence (SAREF) ontology SAREF4BLDG and its corresponding Smart Data Models (52 models at the time). The generated artifacts have been tested on a Context Broker reference implementation. The tool supports basic ontology structures, and for it to translate more complex structures, further development is needed.

In the era of ever-greater data produced and collected, public health research is often limited by the scarcity of data. To improve this, we propose a data sharing in the form of Data Spaces, which provide technical, business, and legal conditions for an easier and trustworthy data exchange for all the participants. The data must be described in a commonly understandable way, which can be assured by machine-readable ontologies. We compared the semantic interoperability technologies used in the European Data Spaces initiatives and adopted them in our use case of physical development in children and youth. We propose an ontology describing data from the Analysis of Children’s Development in Slovenia (ACDSi) study in the Resource Description Framework (RDF) format and a corresponding Next Generation Systems Interface-Linked Data (NGSI-LD) data model. For this purpose, we developed a tool to generate a NGSI-LD data model using information from an ontology in RDF format. The tool builds on the declaration from the standard that the NGSI-LD information model follows the graph structure of RDF, so that such translation is feasible. The source RDF ontology is analyzed using standardized SPARQL Protocol and RDF Query Language (SPARQL), specifically using Property Path queries. The NGSI-LD data model is generated from the definitions collected in the analysis. Multiple ancestries of classes are also supported, even over multiple or common ontologies. These features may equip the tool to be used more broadly in similar contexts.