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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Using the PDD Behavior Inventory as a Level 2 Screener: A Classification and Regression Trees Analysis.

Ira L Cohen1, Xudong Liu2, Melissa Hudson2

  • 1Department of Psychology, New York State Institute for Basic Research in Developmental Disabilities, 1050 Forest Hill Road, Staten Island, NY, 10314, USA. ira.cohen@opwdd.ny.gov.

Journal of Autism and Developmental Disorders
|June 20, 2016
PubMed
Summary

Classification and Regression Trees (CART) improved Autism Spectrum Disorder (ASD) diagnosis accuracy by over 80% in children. This data mining approach identified distinct ASD subtypes and non-ASD groups, aiding in better discrimination.

Keywords:
Autism Spectrum DisorderData miningDecision treesGenotypeLevel 2 screenersMachine learningMonoamine Oxidase APhenotypeSeizuresSubgroups

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Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Data Science

Background:

  • Accurate differential diagnosis between Autism Spectrum Disorder (ASD) and other neurodevelopmental disorders is challenging.
  • Existing diagnostic tools may have limitations in discriminating between these conditions.

Purpose of the Study:

  • To enhance diagnostic accuracy for Autism Spectrum Disorder (ASD) using a data mining approach.
  • To identify distinct subtypes within ASD and non-ASD populations.

Main Methods:

  • Applied Classification and Regression Trees (CART), a data mining technique, to a large, multi-site dataset of PDD Behavior Inventory (PDDBI) forms.
  • Utilized data from children with and without ASD, including parent and teacher reports.
  • Validated the model on an independent dataset and compared results with ADOS classifications.

Main Results:

  • Achieved over 80% discrimination accuracy between ASD and similar disorders.
  • Demonstrated generalization of accuracy across age groups, sites, and to an independent validation set.
  • Identified three ASD subtypes (minimally verbal, verbal, atypical) and two non-ASD subtypes (social pragmatic problems, good social skills).

Conclusions:

  • CART analysis significantly improves the accuracy of differentiating ASD from other neurodevelopmental disorders.
  • The identified subtypes offer a more nuanced understanding of ASD heterogeneity and non-ASD developmental profiles.
  • Parental PDDBI reports were more effective, with cross-informant agreement increasing diagnostic sensitivity.