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

• URL: http://arxiv.org/abs/2312.11832v1
• Date: Tue, 19 Dec 2023 03:48:39 GMT
• Title: 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
• Authors: Zeinab Zakani, Hadi Moradi, Sogand Ghasemzadeh, Maryam Riazi, and Fatemeh Mortazavi
• Abstract summary: The study aimed to validate a video game (FishFinder) for the screening of ADHD using objective measurement of the core symptoms of this disorder. This game was tested on 26 children with ADHD and 26 healthy children aged 5 to 12 years. This system showed 92.3% accuracy, 90% sensitivity, and 93.7% specificity using a combination of in-game and movement features.
• Score: 2.5509157399370554
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. results: This system showed 92.3% accuracy, 90% sensitivity, and 93.7% specificity using a combination of in-game and movement features. 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.

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