Predicting Language Difficulties in Middle Childhood From Early Developmental Milestones: A Comparison of Traditional Regression and Machine Learning Techniques Purpose The current study aimed to compare traditional logistic regression models with machine learning algorithms to investigate the predictive ability of (a) communication performance at 3 years old on language outcomes at 10 years old and (b) broader developmental skills (motor, social, and adaptive) at 3 years old on language ... Research Article
Research Article  |   August 08, 2018
Predicting Language Difficulties in Middle Childhood From Early Developmental Milestones: A Comparison of Traditional Regression and Machine Learning Techniques
 
Author Affiliations & Notes
  • Rebecca Armstrong
    School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Australia
    Centre for Clinical Research, University of Queensland, Brisbane, Australia
    Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
  • Martyn Symons
    Telethon Kids Institute, University of Western Australia, Perth
    National Health and Medical Research Council (NHMRC) Fetal Alcohol Spectrum Disorder (FASD) Research Australia, Centre of Research Excellence, Perth
  • James G. Scott
    Centre for Clinical Research, University of Queensland, Brisbane, Australia
    Metro North Mental Health, Royal Brisbane and Women's Hospital, Australia
  • Wendy L. Arnott
    School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Australia
    Hear and Say, Brisbane, Australia
  • David A. Copland
    School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Australia
    Centre for Clinical Research, University of Queensland, Brisbane, Australia
  • Katie L. McMahon
    Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
  • Andrew J. O. Whitehouse
    Telethon Kids Institute, University of Western Australia, Perth
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication. ×
  • Katie McMahon is now at the School of Clinical Science, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
    Katie McMahon is now at the School of Clinical Science, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.×
  • Correspondence to Rebecca Armstrong: r.armstrong@uqconnect.edu.au
  • Editor-in-Chief: Sean Redmond
    Editor-in-Chief: Sean Redmond×
  • Editor: Jan de Jong
    Editor: Jan de Jong×
Article Information
Language / Research Articles
Research Article   |   August 08, 2018
Predicting Language Difficulties in Middle Childhood From Early Developmental Milestones: A Comparison of Traditional Regression and Machine Learning Techniques
Journal of Speech, Language, and Hearing Research, August 2018, Vol. 61, 1926-1944. doi:10.1044/2018_JSLHR-L-17-0210
History: Received June 1, 2017 , Revised September 22, 2017 , Accepted January 15, 2018
 
Journal of Speech, Language, and Hearing Research, August 2018, Vol. 61, 1926-1944. doi:10.1044/2018_JSLHR-L-17-0210
History: Received June 1, 2017; Revised September 22, 2017; Accepted January 15, 2018

Purpose The current study aimed to compare traditional logistic regression models with machine learning algorithms to investigate the predictive ability of (a) communication performance at 3 years old on language outcomes at 10 years old and (b) broader developmental skills (motor, social, and adaptive) at 3 years old on language outcomes at 10 years old.

Method Participants (N = 1,322) were drawn from the Western Australian Pregnancy Cohort (Raine) Study (Straker et al., 2017). A general developmental screener, the Infant Monitoring Questionnaire (Squires, Bricker, & Potter, 1990), was completed by caregivers at the 3-year follow-up. Language ability at 10 years old was assessed using the Clinical Evaluation of Language Fundamentals–Third Edition (Semel, Wiig, & Secord, 1995). Logistic regression models and interpretable machine learning algorithms were used to assess predictive abilities of early developmental milestones for later language outcomes.

Results Overall, the findings showed that prediction accuracies were comparable between logistic regression and machine learning models using communication-only performance as well as performance on communication and broader developmental domains to predict language performance at 10 years old. Decision trees are incorporated to visually present these findings but must be interpreted with caution because of the poor accuracy of the models overall.

Conclusions The current study provides preliminary evidence that machine learning algorithms provide equivalent predictive accuracy to traditional methods. Furthermore, the inclusion of broader developmental skills did not improve predictive capability. Assessment of language at more than 1 time point is necessary to ensure children whose language delays emerge later are identified and supported.

Supplemental Material https://doi.org/10.23641/asha.6879719

Acknowledgments
The Raine Study is funded by the Raine Medical Research Foundation; the National Health and Medical Research Council; The University of Western Australia; The UWA Faculty of Medicine, Dentistry and Health Sciences; Curtin University; Edith Cowan University; Telethon Kids Institute; and Women and Infants Research Foundation. Andrew J. O. Whitehouse is funded by a senior research fellowship from the National Health and Medical Research Council (Grant 1077966). David A. Copland is funded by an ARC Future Fellowship (Grant FT100100976) and UQ Vice Chancellor's Research & Teaching Fellowship. We are extremely grateful to all the Raine Study participants and their families who took part in this study and the Raine Study team for cohort coordination and data collection.
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