The Validity of a Machine Learning-Based Video Game in the Objective Screening of Attention Deficit Hyperactivity Disorder in Children Aged 5 to 12 Years

This research was conducted with financial support from the Javaneh Program of the Ministry of Science, Research, and Technology of the Islamic Republic of Iran, and the Cognitive Sciences and Technologies Council of the Islamic Republic of Iran. Correspondence concerning this article should be addressed to Hadi Moradi, Department of Robotics and Artificial Intelligence, University of Tehran, Tehran, Iran. Abstract Objective: Early identification of ADHD is necessary to provide the opportunity for timely treatment. However, screening the symptoms of ADHD on a large scale is not easy. This study aimed to validate a video game (FishFinder) for the screening of ADHD using objective measurement of the core symptoms of this disorder. Method: The FishFinder measures attention and impulsivity through in-game performance and evaluates the child's hyperactivity using smartphone motion sensors. This game was tested on 26 children with ADHD and 26 healthy children aged 5 to 12 years. A Support Vector Machine was employed to detect children with ADHD. Conclusions: The FishFinder demonstrated a strong ability to identify ADHD in children. So, this game can be used as an affordable, accessible, and enjoyable method for the objective screening of ADHD. The Validity of a Machine Learning-Based Video Game in the Objective Screening of Attention Deficit Hyperactivity Disorder in Children Aged 5 to 12 Years Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common childhood disorders with a prevalence of about 7.2% (Thomas et al., 2015).