Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of PakistanSalman Mansoor, Shoab Saadat, Sarah Noaman, Hamza Hassan Khan, Salman Assad..
The purpose of this study was to use machine learning algorithms to predict the probability of a child to have a certain attention deficit hyperactive disorder (ADHD) score under a given set of conditions.
This was a cross-sectional survey which employed non-probability convenient sampling technique conducted at two schools in Islamabad, Pakistan. Using the latest version of Konstanz Information Miner (KNIME) Analytics, several machine learning algorithms were tested.
The area under the curve (AUC) for classification tree was 60.8% with a precision of 75.6% for the prediction of an ADHD score of 20 or more and the probability of 21.3% for a child to have an ADHD score of 20 or more. Important variables associated with a higher ADHD score included fathers profession, school of the child, and the class of child.
This study shows that machine learning approach may be useful in developing a robust predictive model. Use of predictive model may allow use of limited resources towards assessment of children with higher probability of ADHD.
Key words: Attention deficit hyperactivity disorder (ADHD), Pakistan, Behavior rating scales, Machine learning approach