Volume 7, Issue 2 (7-2019)                   Jorjani Biomed J 2019, 7(2): 49-60 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Behnampour A, Biglarian A, Bakhshi E. Application of fuzzy logistic regression in modeling the severity of autism spectrum disorder. Jorjani Biomed J. 2019; 7 (2) :49-60
URL: http://goums.ac.ir/jorjanijournal/article-1-657-en.html
1- University of Social Welfare and Rehabilitation Sciences
2- University of Social Welfare and Rehabilitation Sciences , abiglarian@uswr.ac.ir
Abstract:   (604 Views)
Background and objectives: Autism spectrum disorder (ASD) is a childhood neurodevelopmental disorder and according to DSM-5 classification, its severity includes three levels: requiring support, requiring substantial support, and requiring very substantial support. This classification is unclear from a possible perspective and from a fuzzy point of view; it has a degree of uncertainty. The purpose of this study is to predict the severity of autism disorder by fuzzy logistic regression.
Methods: In this cross-sectional study, 22 children with ASD which referred to the rehabilitation centers of Gorgan in 2017 were used as a research sample. Therapist's viewpoint about the severity of the disorder that is measured by linguistic terms (low, moderate, high) was considered as fuzzy output variable. In addition, to determine the prediction model for the severity of autism, a fuzzy logistic regression model was used. In this sense parameters were estimated by least square estimations (LSE) and least absolute deviations (LAD) methods and then the two methods were compared using goodness-of-fit index.
Results: The age of children varied from 6 to 17 years old with mean of 10.44± 3.33 years. Also, the goodness-of-fit index for the model that was estimated by the LAD method was 0.0634, and this value was less than the LSE method (0.1255). The estimated model by the LAD indicates that with the constant of the values of other variables, with each unit increase in the variables of age, male gender, raw score of stereotypical movements, communication and social interaction subscales, possibilistic odds of severity of autism disorder varied about 0.67 (decrease), 0.362 (decrease), 0.098 (increase), 0.019 (increase) and 0.097 (increase) respectively.
Conclusion: The LAD method was better than LSE in parameter estimation. So, the estimated model by this method can be used to predict the severity of autism disorder for new patients who referred to rehabilitation centers and according to predicted severity of the disorder, proper treatments for children can be initiated.
Full-Text [PDF 604 kb]   (252 Downloads)    
Type of Article: Original article | Subject: Bio-statistics
Received: 2019/02/7 | Revised: 2020/01/27 | Accepted: 2019/04/10

1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub; 2013 May 22. [Google Scholar]
2. SARABI JM, Hasanadadi A, Mashhadi A, Asgharinekah M. The effects of parent education and skill training program on stress of mothers of children with autism. 2012. [Google Scholar]
3. Volkmar FR, Paul R, Klin A, Cohen DJ. Handbook of Autism and Pervasive Developmental Disorders, Assessment, Interventions, and Policy: Wiley; 2007. [Google Scholar]
4. Kanner L. Autistic disturbances of affective contact. Nervous child. 1943;2(3):217-50. [Google Scholar]
5. Soto S, Linas K, Jacobstein D, Biel M, Migdal T, Anthony BJ. A review of cultural adaptations of screening tools for autism spectrum disorders. Autism. 2015;19(6):646-61. [DOI] [Google Scholar]
6. Howlin P, Asgharian A. The diagnosis of autism and Asperger syndrome: findings from a survey of 770 families. Developmental medicine and child neurology. 1999;41(12):834-9. [DOI] [Google Scholar]
7. Esbati M, Roberts JM. Autism treatment and family support models review. Iranian Rehabilitation Journal. 2009;7(1):44-9. [Google Scholar]
8. Pourhidar M, Dadkhah A. The Effects of Individual and Group Training on General Health and Stress of Parents of Children with Autism Spectrum Disorders. Iranian Rehabilitation Journal. 2015;13(4):110-5. [Google Scholar]
9. Rice C. Prevalence of autism spectrum disorders--Autism and developmental disabilities monitoring network, United States, 2006. 2009. [DOI] [Google Scholar]
10. Baio J. Prevalence of Autism Spectrum Disorders: Autism and Developmental Disabilities Monitoring Network, 14 Sites, United States, 2008. Morbidity and Mortality Weekly Report. Surveillance Summaries. Volume 61, Number 3. Centers for Disease Control and Prevention. 2012. [Google Scholar]
11. Baio J. Prevalence of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, United States, 2010. 2014. [Google Scholar]
12. Bozorgnia A, Malekpour M, Abedi A, editors. Prevalence of autism in children 6 to 12 years old Shhrkrd 2009-2010. Regional Conference on Child and Adolescent Psychology; 2011. [Google Scholar]
13. Samadi SA, Mahmoodizadeh A, McConkey R. A national study of the prevalence of autism among five-year-old children in Iran. Autism. 2012;16(1):5-14. [DOI] [Google Scholar]
14. Samadi SA, McConkey R. Screening for autism in Iranian preschoolers: Contrasting M-CHAT and a scale developed in Iran. Journal of autism and developmental disorders. 2015;45(9):2908-16. [DOI] [Google Scholar]
15. Rahbar Karbasdehi E, Abolghasemi A, Rahbar Karbasdehi F. Alexithymia and Personality Factors Among Students With and Without Autism Spectrum Disorder. Iranian Rehabilitation Journal. 2018;16(1):77-82. [DOI] [Google Scholar]
16. Vakilizadeh N, Abedi A, Mohseni Ezhiyeh A. Investigating Validity and Reliability of Early Screening for Autistic Traits-Persian Version (ESAT-PV) in Toddlers (Persian). Archives of Rehabilitation. 2017;18(3):182-93. [DOI] [Google Scholar]
17. Vakilizadeh N, Abedi A, Mohseni Ezhiyeh A, Pishghadam E. Effectiveness of family-based early intervention on the degree of joint attention (responding) of the children with autism spectrum disorder: A single-subject study. Journal of Rehabiltation. 2016;17(1):42-53. [DOI] [Google Scholar]
18. Ganji M. Abnormal psychology based on DSM-5. Tehran: savalan Publications. 2013:260-1.
19. Karimzadeh M, Baneshi AR, Dehghan Tezerjani M, Tayyebi Sough Z. Normalization of Pervasive Developmental Disorder Screening Test. Archives of Rehabilitation. 2018;19(2):116-25. [DOI] [Google Scholar]
20. Ahmadi SJ, Hemmatian ST, Khalili Z. Investigation of the psychometric features of the GARS (persion). Research in Cognitive and Behavioral Journal. 2011;1:87-104. [Google Scholar]
21. Zadeh LA, editor Biological application of the theory of fuzzy sets and systems. The Proceedings of an International Symposium on Biocybernetics of the Central Nervous System; 1969: Little, Brown and Comp.
22. Zadeh LA. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on systems, Man, and Cybernetics. 1973(1):28-44. [DOI] [Google Scholar]
23. Norris D, Pilsworth B, Baldwin J. Medical diagnosis from patient records-a method using fuzzy discrimination and connectivity analyses. Fuzzy Sets and Systems. 1987;23(1):73-87. [DOI] [Google Scholar]
24. Pourahmad S, Ayatollahi SMT, Taheri SM, Agahi ZH. Fuzzy logistic regression based on the least squares approach with application in clinical studies. Computers & Mathematics with Applications. 2011;62(9):3353-65. [DOI] [Google Scholar]
25. Pourahmad S, Ayatollahi T, Mohammad S, Taheri SM. Fuzzy logistic regression: a new possibilistic model and its application in clinical vague status. Iranian Journal of Fuzzy Systems. 2011;8(1):1-17. [Google Scholar]
26. Innocent PR, John RI. Computer aided fuzzy medical diagnosis. Information Sciences. 2004;162(2):81-104. [DOI] [Google Scholar]
27. Jafelice RM, de Barros LC, Bassanezi RC, Gomide F. Fuzzy modeling in symptomatic HIV virus infected population. Bulletin of Mathematical Biology. 2004;66(6):1597-620. [DOI] [Google Scholar]
28. Namdari M, Taheri SM, Abadi A, Rezaei M, Kalantari N, editors. Possibilistic logistic regression for fuzzy categorical response data. Fuzzy Systems (FUZZ), 2013 IEEE International Conference on; 2013: IEEE. [DOI] [Google Scholar]
29. Ahmadi S, Safari T, Hemmatian M, Khalili Z. The psychometric properties of Gilliam autism rating scale (GARS) (Persian). Researches of Congnitive and Bihavioral Sciences. 2011(1):1. [Google Scholar]
30. Namdari M, Yoon JH, Abadi A, Taheri SM, Choi SH. Fuzzy logistic regression with least absolute deviations estimators. Soft Computing. 2015;19(4):909-17. [DOI] [Google Scholar]
31. Choi SH, Buckley JJ. Fuzzy regression using least absolute deviation estimators. Soft Computing-A Fusion of Foundations, Methodologies and Applications. 2008;12(3):257-63. [DOI] [Google Scholar]
32. Kelkinnama M, Taheri S. Fuzzy least-absolutes regression using shape preserving operations. Information Sciences. 2012;214:105-20. [DOI] [Google Scholar]
33. Taheri SM, Kelkinnama M. Fuzzy linear regression based on least absolute deviations. Iranian Journal of Fuzzy Systems. 2012;9.1. Association AP. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®): American Psychiatric Publishing; 2013. [Google Scholar]

Add your comments about this article : Your username or Email:

Send email to the article author

© 2020 All Rights Reserved | Jorjani Biomedicine Journal

Designed & Developed by : Yektaweb