Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

video

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

sound

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Version

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

View:

160
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Download:

85
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Cites:

Information Journal Paper

Title

Application of fuzzy logistic regression in modeling the severity of autism spectrum disorder

Pages

  49-60

Abstract

 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.

Cites

  • No record.

    • References

    Cite

    APA: Copy

    Behnampour, Ali, BAKHSHI, ENAYATOLAH, & BIGLARIAN, AKBAR. (2019). Application of fuzzy logistic regression in modeling the severity of autism spectrum disorder. JORJANI BIOMEDICINE JOURNAL, 7(2), 49-60. SID. https://sid.ir/paper/739683/en

    Vancouver: Copy

    Behnampour Ali, BAKHSHI ENAYATOLAH, BIGLARIAN AKBAR. Application of fuzzy logistic regression in modeling the severity of autism spectrum disorder. JORJANI BIOMEDICINE JOURNAL[Internet]. 2019;7(2):49-60. Available from: https://sid.ir/paper/739683/en

    IEEE: Copy

    Ali Behnampour, ENAYATOLAH BAKHSHI, and AKBAR BIGLARIAN, “Application of fuzzy logistic regression in modeling the severity of autism spectrum disorder,” JORJANI BIOMEDICINE JOURNAL, vol. 7, no. 2, pp. 49–60, 2019, [Online]. Available: https://sid.ir/paper/739683/en

    Related Journal Papers

  • No record.

    • Related Seminar Papers

  • No record.

    • Related Plans

  • No record.

    • Recommended Workshops






    Move to top