-طبقه بندی شدت اختلال اُتیسم بااستفاده از روش‌های فازی مبتنی بر محاسبات نرم | ||
| مدیریت مهندسی و رایانش نرم | ||
| مقاله 5، دوره 8، شماره 2 - شماره پیاپی 15، مهر 1401، صفحه 72-91 اصل مقاله (2.17 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| ناهید صابری پور1؛ مهدی مزینانی* 2؛ راحیل حسینی3 | ||
| 1کارشناسی ارشد مهندسی کامپیوتر، دانشکده فنی مهندسی، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران. رایانامه: saberipour.n@gmail.com | ||
| 2استادیار، دانشکده فنی‌‌مهندسی، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران. رایانامه: mahdi.mazinani@qodsiau.ac.ir | ||
| 3استادیار، دانشکده فنی‌‌مهندسی، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران. رایانامه: rahil.hosseini@gmail.com | ||
| چکیده | ||
| جمعیت قابل‌ملاحظه‌ای از افراد، در هر جامعه‌ای مبتلا به اختلال اُتیسم هستند. یکی از معضلات در زمینه تشخیص اختلال اُتیسم وجود عدم قطعیت در تعیین سطح شدت این بیماری است. بدین منظور در این پژوهش برای بر طرف نمودن این مشکل روش‌های مبتنی بر سیستم‌های فازی ارائه گردیده است. روش‌های ارائه‌شده بر روی 112 داده مربوط به کودک و نوجوان بین گروه سنی ۳ تا ۱۴ سال است. که از مراکز مختلف توان‌بخشی واقع در تهران جمع‌آوری و اعمال گردیده است. میانگین صحت عملکرد روش‌های مطرح‌شده بااستفاده از روش الگوریتم ژنتیک با میزان سطح زیر منحنی ROC 4/97 درصد از قابلیت اطمینان و کارایی بهتری در مقایسه با سایر روش‌های پیشنهادی (الگوریتم سیستم استنتاج عصبی فازی تطبیقی) در این پژوهش برخوردار است. سیستم طراحی‌شده در این مقاله می‌تواند به‌عنوان یک روش کمک تشخیص پزشکی برای پزشکان مورداستفاده قرار گیرد. | ||
| کلیدواژه‌ها | ||
| اختلال اُتیسم؛ الگوریتم ژنتیک؛ تست‌گارز؛ سیستم استنتاج عصبی فازی تطبیقی؛ سیستم فازی | ||
| عنوان مقاله [English] | ||
| Classification of Autism Disorder Severity Using Fuzzy Methods Based on Soft Computing | ||
| نویسندگان [English] | ||
| Nahid Saberipour1؛ Mahdi Mazinani2؛ Rahil Hosseini3 | ||
| 1Master of Computer Engineering, Faculty of Engineering, Ghods Branch, Islamic Azad University, Tehran, Iran. Email: saberipour.n@gmail.com | ||
| 2Assistant Professor, Faculty of Engineering, Ghods Branch, Islamic Azad University, Tehran, Iran. Email: mahdi.mazinani@qodsiau.ac.ir | ||
| 3Assistant Professor, Faculty of Engineering, Ghods Branch, Islamic Azad University, Tehran, Iran. Email: rahil.hosseini@gmail.com | ||
| چکیده [English] | ||
| A significant proportion of population in each community suffer from autism disorder. One of the challenges in diagnosing autism is the uncertainty in determining the severity of the disease. To this end, fuzzy systems based methods have been adopted in this study. The presented methods are based on 112 data driven from children and adolescents between the ages of 3 to 14 years. These data were collected from various rehabilitation centers in Tehran. The average performance accuracy of the proposed methods Using Genetic Algorithm with area under curve ROC compared to other methods (adaptive fuzzy neural inference system algorithm) proved to be 97/4% more reliable and efficient. The system designed in this article can be used as a medical diagnostics help tool for physicians. | ||
| کلیدواژه‌ها [English] | ||
| Autism Disorder, Adaptive Neural-Fuzzy Inference System, GARS test, Genetic Algorithm, Fuzzy System | ||
| مراجع | ||
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Ahmadlou, M., Adeli, H., & Adeli, A. (2012). Fuzzy Synchronization Likelihood-wavelet methodology for diagnosis of autism spectrum disorder. Journal of Neuroscience Methods, 211(2), 203–209. (in persian) DOI: https://doi.org/10.1016/j.jneumeth.2012.08.020 Ahsan, R., Chowdhury, T. T., Ahmed, W., Mahia, M. A., Mishma, T., Mishal, M. R., & Rahman, R. M. (2019). Prediction of autism severity level in Bangladesh using fuzzy logic: FIS and ANFIS. In Advances in Intelligent Systems and Computing (Vol. 833, pp. 201–210). DOI: https://doi.org/10.1007/978-3-319-98678-4_22 Akbari Bayatiani, Z. (2018). Autism Spectrum Disorder from Diagnosis to Treatment. The Neuroscience Journal of Shefaye Khatam, 6(4), 93–101. (in persian) DOI: https://doi.org/10.29252/shefa.6.4.93 Al-diabat, M. (2018). Fuzzy data mining for autism classification of children. International Journal of Advanced Computer Science and Applications, 9(7), 11–17. DOI: https://doi.org/10.14569/IJACSA.2018.090702 Arthi, K., & Tamilarasi, A. (2008). Prediction of autistic disorder using neuro fuzzy system by applying ANN technique. International Journal of Developmental Neuroscience, 26(7), 699–704. DOI: https://doi.org/10.1016/j.ijdevneu.2008.07.013Get rights and content Calderoni, S., Retico, A., Biagi, L., Tancredi, R., Muratori, F., & Tosetti, M. (2012). Female children with autism spectrum disorder: An insight from mass-univariate and pattern classification analyses. NeuroImage, 59(2), 1013–1022. DOI: https://doi.org/10.1016/j.neuroimage.2011.08.070 Cohen, I. L., Sudhalter, V., Landon-Jimenez, D., & Keogh, M. (1993). A neural network approach to the classification of autism. Journal of Autism and Developmental Disorders, 23(3), 443–466. DOI: https://doi.org/10.1007/BF01046050 Ecker, C., Marquand, A., Mourão-Miranda, J., Johnston, P., Daly, E. M., Brammer, M. J., Maltezos, S., Murphy, C. M., Robertson, D., Williams, S. C., & Murphy, D. G. M. (2010). Describing the brain in autism in five dimensions - Magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. Journal of Neuroscience, 30(32), 10612– 10623 DOI: https://doi.org/10.1523/JNEUROSCI.5413-09.2010 Gambini, O., Barbieri, V., & Scarone, S. (2004). Theory of Mind in schizophrenia: First person vs third person perspective. Consciousness and Cognition, 13(1), 39–46. DOI: https://doi.org/10.1016/S1053-8100(03)00046-1 Ganji, M. (2015). Complete guide to changes and essentials DSM-5 (Ganji, Meh). Tehran: Savalan. (in persian) DOI: https://doi.org/10.5812/ijpbs.87974 Isa, N. R. M., Yusoff, M., Khalid, N. E., Tahir, N., & Binti Nikmat, A. W. (2015). Autism severity level detection using fuzzy expert system. In 2014 IEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014 (pp. 218–223). IEEE. DOI: https://doi.org/10.1109/ROMA.2014.7295891 Kannappan, A., Tamilarasi, A., & Papageorgiou, E. I. (2011). Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder. Expert Systems with Applications, 38(3), 1282–1292 DOI: https://doi.org/10.1016/j.eswa.2010.06.069 Khalili, Z., Hemmatian, M., Safari, T., & Ahmadi, S. J. (2012). The Psychometric Properties of Gilliam Autism Rating Scale (GARS). Journal of Cognitive 14and Behavioral Sciences Research, 78–104. (in persian) DOI: https://doi.org/10.3390/children9030434 Khan, S., & Alshara, M. (2019a). Fuzzy Data Mining Utilization to Classify Kids with Autism. International Journal of Computer Science and Network Security, 19(2), 147–154. DOI: https://doi.org/10.14569/IJACSA.2018.090702 Khanmohammadi, S., & Jasbi, J. (2011). Introduction to Fuzzy Applied Logic. Tehran: Islamic Azad University. (in persian) DOI: https://doi.org/10.30495/tfss.2023.1973510.1056 Naseh, H. (2012). Along with autism from diagnosis to treatment for children who want but do not know how. (M. M. Shariati Bagheri, Ed.). Tehran: Dangier. Retrieved from. (in persian) DOI: https://doi.org/10.1002/pits.22808 Papageorgiou, E. I., & Kannappan, A. (2012). Fuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification. Applied Soft Computing Journal, 12(12), 3798–3809 DOI: https://doi.org/10.1016/j.asoc.2012.03.064 Pratap, A., Kanimozhiselvi, C. S., Pramod, K. V., & Vijayakumar, R. (2014). Functional fuzzy based autism assessment support system. International Journal of Engineering and Technology, 6(5), 2105–2114. DOI: https://doi.org/10.3991/ijet.v15i06.11231 Pream Sudha, V., & Vijaya, M. S. (2019). Machine learning-based model for identification of syndromic autism spectrum disorder. Studies in Computational Intelligence, 771, 141–148 DOI: https://doi.org/10.1007/978-981-10-8797-4_16 Puerto, E., Aguilar, J., López, C., & Chávez, D. (2019). Using Multilayer Fuzzy Cognitive Maps to diagnose Autism Spectrum Disorder. Applied Soft Computing Journal, 75, 58–71. DOI: https://doi.org/10.1016/j.asoc.2018.10.034 Report, M. W. (2014). Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2010. Morbidity and Mortality Weekly Report. Surveillance Summaries (Washington, D.C. : 2002). DOI: https://doi.org/10.15585/mmwr.ss6706a1 Russell, S., & McCloskey, C. R. (2016). Parent Perceptions of Care Received by Children With an Autism Spectrum Disorder. Journal of Pediatric Nursing, 31(1), 21–31 DOI: https://doi.org/10.1016/j.pedn.2015.11.002 Saberipour, N., Mazinani, M., & Hosseini, R. (2018). Presentation of a Fuzzy Intelligent Model Based on the Lookup Table Rule Method and Hierarchical Clustering Agglomerative to for Autism Cassification Based on GARS test. In 4th International Conference on Modern Studies in Computer Science & IT (pp. 1–14). Tehran.(in persian) DOI: https://doi.org/10.1007/978-3-319-42972-4_9 Sakishita, M., Ogawa, C., Tsuchiya, K. J., Iwabuchi, T., Kishimoto, T., & Kano, Y. (2020). Autism Spectrum Disordera's Severity Prediction Model Using Utterance Features for Automatic Diagnosis Support. In Studies in Computational Intelligence (Vol. 843, pp. 83–95). DOI: https://doi.org/10.1007/978-3-030-24409-5_8 Shams, W. K., Wahab, A., & Qidwai, U. A. (2012). Fuzzy model for detection and estimation of the degree of autism spectrum disorder. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7666 LNCS, pp. 372– 379). DOI: https://doi.org/10.1007/978-3-642-34478-7_46 Shandley, K., & Austin, D. W. (2011). Autism spectrum disorders. A Critical Introduction to DSM (First Era). Tehran: Doran. (in persian) DOI: https://doi.org/10.5812/ijpbs-135500 Sharma, A., Khosla, A., Khosla, M., & Rao, Y. (2018). Fast and Accurate Diagnosis of Autism (FADA): a novel hierarchical fuzzy system based autism detection tool. Australasian Physical & Engineering Sciences in Medicine, 41(3), 757–772. DOI: https://doi.org/10.1007/s13246-018-0666-3 Vakilizadeh, N., Abedi, A., & Mohseni Ezhiyeh, A. (2017). Investigating Validity and Reliability of Early Screening for Autistic Traits-Persian Version (ESAT-PV) in Toddlers. Journal of Rehabilitation, 18(3), 182–193. (in persian) DOI: https://doi.org/10.21859/jrehab-1803182 Wibowo, A., Fauziah, D., Yuliani, Y., Rahayu, Y., Riyanto, A., & Oktapiani, R. (2019). Fuzzy Logic for Autism Screening Test. Journal of Physics: Conference Series, 1179(1), 012015. DOI: https://doi.org/10.1088/1742-6596/1179/1/012015 | ||
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