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
Zeinab Zakani Hadi Moradi Sogand Ghasemzadeh Maryam Riazi Fatemeh Mortazavi
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.