Using hybrid systems in the construction of expert systems in the identification of cognitive and motor problems in children and young people

This paper proposes the use of intelligent hybrid systems based on the concepts of artificial neural networks and fuzzy systems to assist in the identification of children who have a problem that may impair their motor or cognitive development. Research on children seeking to identify diseases that are listed in the International Classification of Functioning, Disability, and Health (ICF-CY) are the targets of this research. To assist in the disease identification studies, a database was provided for researchers from all over the world to develop techniques to aid in the creation of specialist systems based on fuzzy rules to assist in the detection of healthy children with psychological or motor dysfunction. This paper used a smart model capable of generating fuzzy rules to construct a predictor model that helps to diagnose some of these problems in children or adolescents. The results obtained were promising, obtaining better accuracy indexes than the initial studies, confirming that the approach is feasible to identify children with ICF-CY.