Abstract:
According to the worldwide WHO statistics, more than 20% of Children and adolescents are
affected from a mental illness. Several psychological studies show that most of the mental
disorders are starting from childhood and in many cases continue to adulthood. Attention deficit hyperactivity disorder (ADHD) is recognised as the most common behavioural disorder
in school aged children. The worldwide ADHD prevalence is estimated as 5-10% of children
in any condition.
Detection is one of the most important defences to helping those children who express
disruptive behaviours or any other symptoms of ADHD. While there are many tools or
applications to detect adult metal health conditions, there is a gap around detection in children
and early adolescent. Especially when it comes to behavioural disorders like ADHD there are
many barriers to identifying disorder and seeking proper treatment.
Child Behaviour Analyzer address the above problem of identifying ADHD in children and
adolescent by analysing their behavioural symptoms. As well the solution will address the
reliability problem of SNAP-IV rating scale which is widely used in ADHD clinical diagnosis.
Support vector machine (SVM) classification model approach was used to predict the current
ADHD status of the children.
