Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution.

Featured Application: This study is part of an ongoing research project titled "Smart Computing Models, Sensors, and Early diagnostic speech and language deficiencies indicators in Child Communication", with the acronym "SmartSpeech". The SmartSpeech project aims to assist clinicians in decision making regarding early diagnosis for children with neurodevelopmental disorders. SmartSpeech employs a serious game designed explicitly by the interdisciplinary team for this project, with activities aiming to evaluate the child's developmental profile. The game is implemented in a tablet application and utilizes voice, and biomarkers of heart rate and gaze, for additional physiological measurements. A back-end system supports user registration, data collection, data analysis, and decision making. The potential application of this work is to allow the SmartSpeech machine learning model to better capture underlying patterns in the data, determine the most effective feature construction techniques for the given problem, and employ the results of this study to enhance the screening automated prediction results of the machine learning model in neurodevelopmental disorders. Developmental domains refer to different areas of a child's growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child's progress and identify potential areas of concern. Nevertheless, due to the high level of heterogeneity and overlap, neurodevelopmental disorders may go undiagnosed in children for a crucial period. Detecting neurodevelopmental disorders at an early stage is fundamental. Digital tools like artificial intelligence can help clinicians with the early detection process. To achieve this, a new method has been proposed that creates artificial features from the original ones derived from the SmartSpeech project, using a feature construction procedure guided by the Grammatical Evolution technique. The new features from a machine learning model are used to predict neurodevelopmental disorders. Comparative experiments demonstrated that using the feature creation method outperformed other machine learning methods for predicting neurodevelopmental disorders. In many cases, the reduction in the test error reaches up to 65% to the next better one. [ABSTRACT FROM AUTHOR]