Abstract
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.
Similar content being viewed by others
References
Adi-Japha, E., Fox, O., & Karni, A. (2011). Atypical acquisition and atypical expression of memory consolidation gains in a motor skill in young female adults with ADHD. Research in Developmental Disabilities, 32, 1011–1020. doi:10.1016/j.ridd.2011.01.048
Agresti, A. (2002). Categorical Data Analysis. Hoboken: Wiley.
American Psychiatric Association (2000). Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition-Text Revision. Author: Arlington, VA.
Behere, A., Shahani, L., Noggle, C. A., & Dean, R. (2012). Motor functioning in autistic spectrum disorders: A preliminary analysis. The Journal of Neuropsychiatry and Clinical Neurosciences, 24, 87–94.
Brieman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Monterey: Wadsworth publishing.
Bruchmüller, K., Margraf, J., & Schneider, S. (2012). Is ADHD diagnosed in accord with diagnostic criteria? Overdiagnosis and influence of client gender on diagnosis. Journal of Consulting and Clinical Psychology, 80, 128–138.
Bühlmann, P., & Yu, B. (2002). Analyzing bagging. Annals of Statistics, 30, 927–961.
Carmona, S. Vilarroya, O., Bielsa, A. Tremols, V., Soliva, J. C., Rovira, M., … Bulbena, A. (2005). Global and regional gray matter reductions in ADHD: a voxel-based morphometric study. Neuroscience Letters, 389, 88-93.
Davis, A. S., & Dean, R. S. (2010). The Dean-Woodcock Sensory-Motor Battery. In A. S. Davis (Ed.), Handbook of Pediatric Neuropsychology (pp. 335–342). New York: Springer Publishing.
Davis, A. S., Finch, W. H., Dean, R. S., & Woodcock, R. W. (2006). Cortical and subcortical constructs of the Dean-Woodcock Sensory Motor Battery: A construct validity study. International Journal of Neuroscience, 116, 1157–1171.
Davis, A. S., Finch, W. H., Trinkle, J. M., Dean, R. S., & Woodcock, R. W. (2007). Classification and regression tree analysis of a neurologically impaired and normal sample using sensory-motor tasks. International Journal of Neuroscience, 117, 11–23.
Davis, A. S., Pass, L. A., Finch, W. H., & Dean, R. S. (2009). The canonical relationship between sensory-motor functioning and cognitive processing in children with Attention-Deficit/Hyperactivity Disorder. Archives of Clinical Neuropsychology, 24, 273–286.
Davis, A. S., Mazur-Mosiewicz, A., & Dean, R. S. (2011). The Presence and predictive value of astereognosis and agraphesthesia in patients with Alzheimer’s disease. Applied Neuropsychology, 17, 262–266.
Dean, R. S., & Woodcock, R. W. (2003a). Dean-Woodcock Neuropsychological Battery. Itasca: Riverside Publishing.
Dean, R. S., & Woodcock, R. W. (2003b). Examiner’s manual: Dean-Woodcock Neuropsychological Battery. Itasca: Riverside Publishing.
Dobra, A., & Gehrke, J. E. (2001). Bias Correction in Classification Tree Construction. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2001). Massachusetts: Williams College.
Duerden, E. G., Tannock, R., & Dockstader, C. (2012). Altered cortical morphology in sensorimotor processing regions in adolescents and adults with attention-deficit/hyperactivity disorder. Brain Research, 1445, 82–91.
Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–813.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 39, 1189–1232.
Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics and Data Analysis, 38, 367–378.
Gorman Bozorgpour, E. B., Klorman, R., & Gift, T. E. (2013). Effects of subtype of Attention-Deficit/Hyperactivity Disorder in adults on lateralized readiness potentials during a go/no-go choice reaction time task. Journal of Abnormal Psychology, 122(3), 868–878. doi:10.1037/a0033992
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. New York: Springer.
Hill, S. K., Lewis, M. N., Dean, R. S., & Woodcock, R. S. (2001). Constructs underlying measures of sensory-motor functioning. Archives of Clinical Neuropsychology, 15, 631–641.
Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15, 651–674.
Iwanaga, R., Ozawa, H., Kawasaki, C., & Tsuchida, R. (2006). Characteristics of the sensory-motor, verbal and cognitive abilities of preschool boys with attention-deficit/hyperactivity disorder combined type. Psychiatry and Clinical Neurosciences, 60, 37–45.
Mostofsky, S. H., Rimrodt, S. L., Schafer, J. G., Boyce, A., Goldberg, M. C., Pekar, J. J., & Denckla, M. B. (2006). Atypical motor and sensory cortex activation in attention-deficit/hyperactivity disorder: a functional magnetic resonance imaging study of simple sequential finger tapping. Biological Psychiatry, 59, 48–56.
Parush, S., Sohmer, H., Steinberg, A., & Kaitz, M. (1997). Somatosensory functioning in children with attention deficit hyperactivity disorder. Developmental Medicine & Child Neurology, 39, 464–468.
Schapire, R. E., & Freund, Y. (2012). Boosting: Foundations and algorithms. Cambridge: The MIT Press.
Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of Classification and Regression Trees, Bagging, and Random Forests. Psychological Methods, 14(4), 323–348.
Washbrook, E., Propper, C., & Sayal, K. (2013). Pre-school hyperactivity/attention problems and educational outcomes in adolescence: prospective longitudinal study. British Journal of Psychiatry, 203, 265–271.
Williams, C. J., Lee, S. S., Fisher, R. A., & Dickerman, L. H. (1999). A comparison of statistical methods for prenatal screening for Down Syndrome. Applied Stochastic Models in Business and Industry, 15(2), 89–101.
Woodcock, R. S., McGrew, K. S., & Mather, N. (2001a). Woodcock-Johnson III Tests of Achievement. Itasca: Riverside Publishing.
Woodcock, R. S., McGrew, K. S., & Mather, N. (2001b). Woodcock-Johnson III Tests of Cognitive Ability. Itasca: Riverside Publishing.
Woodward, H. R., Ridenour, T., Dean, R. S., & Woodcock, R. S. (2002). Generalizability of sensory and motor tests. International Journal of Neuroscience, 112, 1115–1137.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Finch, H.W., Davis, A. & Dean, R.S. Identification of individuals with ADHD using the dean–woodcock sensory motor battery and a boosted tree algorithm. Behav Res 47, 204–215 (2015). https://doi.org/10.3758/s13428-014-0460-4
Published:
Issue Date:
DOI: https://doi.org/10.3758/s13428-014-0460-4