Identification of individuals with ADHD using the Dean-Woodcock sensory motor battery and a boosted tree algorithm.
The accurate and early identification of individuals with pervasive conditions such as attention deficit hyperactivity disorder (ADHD) is crucial to ensuring that they receive appropriate and timely assistance and treatment. Heretofore, identification of such individuals has proven somewhat difficult, typically involving clinical decision making based on descriptions and observations of behavior, in conjunction with the administration of cognitive assessments. The present study reports on the use of a sensory motor battery in conjunction with a recursive partitioning computer algorithm, boosted trees, to develop a prediction heuristic for identifying individuals with ADHD. Results of the study demonstrate that this method is able to do so with accuracy rates of over 95 %, much higher than the popular logistic regression model against which it was compared. Implications of these results for practice are provided.
Ball State University, Muncie, IN, USA, whfinch@bsu.edu.
Holmes W
HW
Finch
Andrew
A
Davis
Raymond S
RS
Dean
D000465:Algorithms; D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis / Q000523:psychology; D042241:Early Diagnosis; D006801:Humans; D016015:Logistic Models; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests; D011597:Psychomotor Performance