Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most thoroughly researched disorders in medicine. Although many data mining techniques have been applied to diagnose the main causes of ADHD, but only few sets of clinical risk factors are considered. So the results produced by such techniques may not represent appropriate ADHD pattern and risk factors appropriately. Environmental factors also play a role in elucidating this disorder. In this study, we have designed a system that can efficiently discover the rules and the risk level of patients based on the given parameter about this disorder, comfortable with making the diagnosis and treating them with ADHD. We consider the relationship among comorbidities of ADHD based on association rule mining (ARM) among these data mining techniques with the help of Apriori Algorithm .The intention of this project is to diagnose the symptoms(patterns) that may result in ADHD in children in the earlier stages by using the Data Mining techniques .
Keywords: ADHD, Data mining, R software, Association Rule Mining, Apriori Algorithm
| DOI: 10.17148/IJARCCE.2019.8616