A comparison of machine learning algorithms for the surveillance of autism spectrum disorder
A comparison of machine learning algorithms for the surveillance of autism spectrum disorder
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Publisher
United States: Public Library of Science
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Language
English
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Publisher
United States: Public Library of Science
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Scope and Contents
Contents
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 rece...
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Full title
A comparison of machine learning algorithms for the surveillance of autism spectrum disorder
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TN_cdi_plos_journals_2297122100
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2297122100
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ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0222907
