title: Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning.
abstract: Although the prevalence of autism spectrum disorder (ASD) has risen sharply in the last few years reaching 1 in 68, the average age of diagnosis in the United States remains close to 4--well past the developmental window when early intervention has the largest gains. This emphasizes the importance of developing accurate methods to detect risk faster than the current standards of care. In the present study, we used machine learning to evaluate one of the best and most widely used instruments for clinical assessment of ASD, the Autism Diagnostic Observation Schedule (ADOS) to test whether only a subset of behaviors can differentiate between children on and off the autism spectrum. ADOS relies on behavioral observation in a clinical setting and consists of four modules, with module 2 reserved for individuals with some vocabulary and module 3 for higher levels of cognitive functioning. We ran eight machine learning algorithms using stepwise backward feature selection on score sheets from modules 2 and 3 from 4540 individuals. We found that 9 of the 28 behaviors captured by items from module 2, and 12 of the 28 behaviors captured by module 3 are sufficient to detect ASD risk with 98.27% and 97.66% accuracy, respectively. A greater than 55% reduction in the number of behaviorals with negligible loss of accuracy across both modules suggests a role for computational and statistical methods to streamline ASD risk detection and screening. These results may help enable development of mobile and parent-directed methods for preliminary risk evaluation and/or clinical triage that reach a larger percentage of the population and help to lower the average age of detection and diagnosis.
authors.affiliation: 1] Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA [2] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
authors.forename: J A
authors.initials: JA
authors.lastname: Kosmicki
authors.affiliation: Graduate Program in Biomedical Informatics, Stanford University, Stanford, CA, USA.
authors.forename: V
authors.initials: V
authors.lastname: Sochat
authors.affiliation: Division of Systems Medicine, Department of Pediatrics and Psychiatry (by courtesy), Stanford University, Stanford, CA, USA.
authors.forename: M
authors.initials: M
authors.lastname: Duda
authors.affiliation: Division of Systems Medicine, Department of Pediatrics and Psychiatry (by courtesy), Stanford University, Stanford, CA, USA.
authors.forename: D P
authors.initials: DP
authors.lastname: Wall
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001321:Autistic Disorder / Q000175:diagnosis* / Q000523:psychology*; D002648:Child; D002652:Child Behavior / Q000523:psychology*; D002675:Child, Preschool; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D007223:Infant; D000069550:Machine Learning / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D011594:Psychometrics; D015203:Reproducibility of Results; D012307:Risk Factors; D055815:Young Adult