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2014, Number 3

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Rev Mex Ing Biomed 2014; 35 (3)

Methodology to weight evaluation areas from autism spectrum disorder ADOS-G test with artificial neural networks and Taguchi method

Reyes M, Ponce P, Grammatikou D, Molina A

Language: English
References: 33
Page: 223-240
PDF size: 1756.27 Kb.


ABSTRACT

Autism diagnosis requires validated diagnostic tools employed by mental health professionals with expertise in autism spectrum disorders. This conventionally requires lengthy information processing and technical understanding of each of the areas evaluated in the tools. Classifying the impact of these areas and proposing a system that can aid experts in the diagnosis is a complex task. This paper presents the methodology used to find the most significant items from the ADOSG tool to detect Autism Spectrum Disorders through Feed-forward Artificial Neural Networks with back-propagation training. The number of cases for the network training data was determined by using the Taguchi method with Orthogonal Arrays reducing the sample size from 531,441 to only 27. The trained network provides an accuracy of 100% with 11 different cases used only for validation, which provides a specificity and sensitivity of 1. The network was used to classify the 12 items from the ADOS-G tool algorithm into three levels of impact for Autism diagnosis: High, Medium and Low. It was found that the items “Showing”, “Shared enjoyment in Interaction” and “Frequency of vocalization directed to others”, are the areas of highest impact for Autism diagnosis. The methodology here presented can be replicated to different Autism diagnosis tests to classify their impact areas as well.


REFERENCES

  1. L. Wing, "The autistic spectrum", The lancet, 350(9093), pp. 1761-1766, 1997.

  2. L. Kanner, "Autistic disturbances of affective contact", Nervous child, vol 2. no.3, pp. 217-250, 1943.

  3. Centers for Disease Control and Prevention. "Prevalence of autism spectrum disorders-Autism and Developmental Disabilities Monitoring Network, 14 sites, United States, 2008", MMWR 2012; vol. 61, No. 3. pp.1-2.

  4. M. Marquez-Caraveo and L. Albores-Gallo, "Autistic spectrum disorders: Diagnostic and therapeutic challenges in Mexico", Salud Mental, Vol. 34 no. 5, pp. 435-441, 2011.

  5. C. Marcín (2013, February,6) "Prevalencia del Autismo en México", [Online]. Available: http://www.clima.org.mx/images/ pdf/prevalencia.pdf.

  6. American Psychiatric Association [APA], "The Diagnostic and Statistical Manual of Mental Disorders: DSM 5.", Arlington, 2013, pp. 50-59.

  7. R. Arias et al., "Diagnostico y Manejo de los Trastornos del Espectro Autista.", IMSS, México, 2012.

  8. M. Marquez-Caraveo et al, "Guía Clínica: Trastornos Generalizados del Desarrollo", Guías Clínicas del Hospital Psiquiátrico Infantil Dr. Juan N Navarro, 2010.

  9. PA. Filipek et al., "The Screening and Diagnosis of Autistic Spectrum Disorders", Journal of Autism and Developmental Disorders, vol.29, no.6, pp. 439-484, 1999.

  10. World Health Organization, "International Statistical Classification of Diseases and Related Health Problems", 10th Revision, 2nd Ed, 2004.

  11. S. Baron-Cohen et al., "Early identification of autism by the Checklist for Autism in Toddlers (CHAT)", Journal of the royal society of medicine, vol. 93, no. 10, pp. 521-525, 2000.

  12. D Robins, et al. (2009). "Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F)" [online]. Available:http://www.autismspeaks.org/ sites/default/files/docs/sciencedocs/mchat/ m-chat-r_f.pdf?v=1.

  13. W. Stone et al., "Brief Report: Screening Tool for Autism in Two-Year-Olds (STAT): Development and Preliminary Data", Journal of Autism and Developmental Disorders, vol. 30, no. 6, pp. 607-612, 2000.

  14. A. Wetherby et al., "Validation of the Infant-Toddler Checklist as a Broadband Screener for Autism Spectrum Disorders from 9 to 24 Months of Age", Autism, vol.12, no.5, pp.487-511, 2008.

  15. C. Chlebowski et al., "Using the Childhood Autism Rating Scale to Diagnose Autism Spectrum Disorders", J Autism Dev Disord, vol.40, no. 7, pp. 787- 799, 2010.

  16. C. Lord et al., "The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism", Autism Dev Disord, vol.30, no. 3, pp. 205-223, 2000.

  17. C. Lord et al., "Autism Diagnostic Interview - Revised: A Revised Version of a Diagnostic Interview for Caregivers of Individuals with Possible Pervasive Developmental Disorders", Autism and Dev Disor, vol. 4, no. 5, pp.659-685, 1994.

  18. K. Gotham, "Standardizing ADOS Scores for a measure of Severity in autism spectrum disorders", J Autism Dev Disord, vol. 39, no. 5, pp. 693-705.

  19. C. Lord et al., "ADOS, Escala de observación para el diagnóstico del autismo", TEA Ediciones, 2008.

  20. P. Ponce. "Inteligencia Artificial con Aplicaciones a la Ingeniería", 1a Ed, Cd. Mexico, Mexico,Alfaomega, 2010, ch. 3, pp.193-234.

  21. G. Borgersen and L. Karlsson, "Supervised learning in artificial neural networks" in IRCSE, Västerås, Sweden, 2008, pp.1-6.

  22. S. Gopal, "Artificial Neural Networks for Spatial Data Analysis", in NCGIA Core Curriculum in GIScience, Boston, MA, 1998.

  23. B. Krose and P. Van Der Smagt (1996). An Introduction to Neural Network [online]. Available: http://www.ieee.org/documents/ieeecitationref.pdf.

  24. W. Baxt, "Application of artificial neural networks to clinical medicine," The Lancet, vol. 346, no. 8983, pp. 1135-1138, 1995.

  25. I. Cohen et al., "A neural network approach to the classification of autism.", J Autism and Dev Dis, vol. 23, no. 3, pp. 443-66, 1993.

  26. K. Srinivasan and S. Veeraraghavan, "Exploration of Autism using Expert Systems", in ITNG’07, Las Vegas, NV 2007. pp. 261-264.

  27. K. Arthi and A. Tamilarasi, "Prediction of autistic disorder using neuro fuzzy system by applying ANN technique", International journal of developmental neuroscience, vol. 26, no. 7, pp. 699-704, 2008.

  28. D. Wall et al., "Use of machine learning to shorten observation-based screening and diagnosis of autism", Translational Psychiatry, vol.2, no. 100, pp. 1-8, 2012.

  29. D.Wall et al., "Use of Artificial Intelligence to shorten the behavioral Diagnosis of Autism", Plos One, vol. 7, no.8, pp. 1-8, 2012.

  30. R. Ranjit, "A Primer on the Taguchi Method", New York: Van Nostrand Reinhold, 1990, pp.1-5.

  31. L. Sun et al., "A New Modeling Technique Based on the ANN and DOE for Interconnects", in ASIC, China, 2001.

  32. The MathWorks,Inc. (2014), "Mathlab Primer R2014a" [Online]. Available: http://www.mathworks.com/help/pdf_doc/ matlab/getstart.pdf.

  33. P. Refaeilzadeh et al., "Cross Validation", Encyclopedia of Database Systems, pp. 532-538, 2009




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