A comparison of machine learning algorithms for the surveillance of autism spectrum disorder.

Journal: PloS one
Published Date: Jan 1, 2019

Abstract

OBJECTIVE: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speeding up this process, but they lag behind human classification accuracy by about 5%. We explore whether more recently available document classification algorithms can close this gap.

Authors

  • Scott H Lee
    Centers for Disease Control and Prevention, Atlanta, GA, USA.
  • Matthew J Maenner
    National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention; Atlanta, GA United States of America.
  • Charles M Heilig
    Centers for Disease Control and Prevention, Atlanta, GA, United States of America.

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