IJISA Vol. 15, No. 4, 8 Aug. 2023
Cover page and Table of Contents: PDF (size: 616KB)
Full Text (PDF, 616KB), PP.26-36
Views: 0 Downloads: 0
Autism Spectrum Disorder, Fuzzy Neural Network, Pattern Classification
Autism spectrum disorder (ASD) is a chronic developmental impairment that impairs a person's ability to communicate and connect with others. In people with ASD, social contact and reciprocal communication are continually jeopardized. People with ASD may require varying degrees of psychological aid in order to gain greater independence, or they may require ongoing supervision and care. Early discovery of ASD results in more time allocated to individual rehabilitation. In this study, we proposed the fuzzy classifier for ASD classification and tested its interpretability with the fuzzy index and Nauck's index to ensure its reliability. Then, the rule base is created with the Gauje tool. The fuzzy rules were then applied to the fuzzy neural network to predict autism. The suggested model is built on the Mamdani rule set and optimized using the backpropagation algorithm. The proposed model uses a heuristic function and pattern evolution to classify dataset. The model is evaluated using the benchmark metrics accuracy and F-measure, and Nauck's index and fuzzy index are employed to quantify interpretability. The proposed model is superior in its ability to accurately detect ASD, with an average accuracy rate of 91% compared to other classifiers.
Rajan Prasad, Praveen Kumar Shukla, "Interpretable Fuzzy System for Early Detection Autism Spectrum Disorder", International Journal of Intelligent Systems and Applications(IJISA), Vol.15, No.4, pp.26-36, 2023. DOI:10.5815/ijisa.2023.04.03
[1]Rice, Catherine E., Michael Rosanoff, Geraldine Dawson, Maureen S. Durkin, Lisa A. Croen, Alison Singer, and Marshalyn Yeargin-Allsopp. "Evaluating changes in the prevalence of the autism spectrum disorders (ASDs)." Public health reviews 34 (2012): 1-22.
[2]Grzadzinski, Rebecca, Marisela Huerta, and Catherine Lord. "DSM-5 and autism spectrum disorders (ASDs): an opportunity for identifying ASD subtypes." Molecular autism 4, no. 1 (2013): 1-6.
[3]Cohen, Simonne, Russell Conduit, Steven W. Lockley, Shantha MW Rajaratnam, and Kim M. Cornish. "The relationship between sleep and behavior in autism spectrum disorder (ASD): a review." Journal of neurodevelopmental disorders 6, no. 1 (2014): 1-10.
[4]Thabtah, Fadi. "Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfillment." In Proceedings of the 1st International Conference on Medical and health Informatics 2017, pp. 1-6. 2017.
[5]Haglund, Nils, SvenOlof Dahlgren, Maria Råstam, Peik Gustafsson, and Karin Källén. "Improvement of autism symptoms after comprehensive intensive early interventions in community settings." Journal of the American Psychiatric Nurses Association 27, no. 6 (2021): 483-495.
[6]Lord, Catherine, Edwin H. Cook, Bennett L. Leventhal, and David G. Amaral. "Autism spectrum disorders." Neuron 28, no. 2 (2000): 355-363.
[7]Johnson, Chris Plauché, and Scott M. Myers. "Identification and evaluation of children with autism spectrum disorders." Pediatrics 120, no. 5 (2007): 1183-1215.
[8]Geschwind, Daniel H. "Genetics of autism spectrum disorders." Trends in cognitive sciences 15, no. 9 (2011): 409-416.
[9]Matson, Johnny L., and Alison M. Kozlowski. "The increasing prevalence of autism spectrum disorders." Research in autism spectrum disorders 5, no. 1 (2011): 418-425.
[10]Shukla, Praveen Kumar, and Surya Prakash Tripathi. "A review on the interpretability-accuracy trade-off in evolutionary multi-objective fuzzy systems (EMOFS)." Information 3, no. 3 (2012): 256-277.
[11]Shukla, Praveen Kumar, and Surya Prakash Tripathi. "A survey on interpretability-accuracy (IA) trade-off in evolutionary fuzzy systems." In 2011 Fifth International Conference on Genetic and Evolutionary Computing, pp. 97-101. IEEE, 2011.
[12]Shukla, Praveen Kumar, and Surya Prakash Tripathi. "A new approach for tuning interval type-2 fuzzy knowledge bases using genetic algorithms." Journal of Uncertainty Analysis and Applications 2, no. 1 (2014): 1-15.
[13]Attaallah, A.; al-Sulbi, K.; Alasiry, A.; Marzougui, M.; Ansar, S.A.; Agrawal, A.; Ansari, M.T.J.; Khan, R.A. Fuzzy-Based Unified Decision-Making Technique to Evaluate Security Risks: A Healthcare Perspective. Mathematics 2023, 11, 2554. https://doi.org/10.3390/math11112554
[14]Zadeh, Lotfi A. "Soft computing and fuzzy logic." IEEE software 11, no. 6 (1994): 48-56.
[15]Zadeh, Lotfi A. "Fuzzy logic, neural networks, and soft computing." In Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh, pp. 775-782. 1996.
[16]Theodoridis, Sergios, and Konstantinos Koutroumbas. Pattern recognition. Elsevier, 2006.
[17]Bezdek, James C. Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media, 2013.
[18]Alonso, Jose M., and Luis Magdalena. "Special issue on interpretable fuzzy systems." Information Sciences 181, no. 20 (2011): 4331-4339.
[19]Cpałka, Krzysztof. Design of interpretable fuzzy systems. Cham: Springer International Publishing, 2017.
[20]Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani et al. "An image is worth 16x16 words: Transformers for image recognition at scale." arXiv preprint arXiv:2010.11929 (2020).
[21]Reddy, D. Raj. "Speech recognition by machine: A review." Proceedings of the IEEE 64, no. 4 (1976): 501-531.
[22]Chowdhary, KR1442, and K. R. Chowdhary. "Natural language processing." Fundamentals of artificial intelligence (2020): 603-649.
[23]Jain, Anil K., Jianchang Mao, and K. Moidin Mohiuddin. "Artificial neural networks: A tutorial." Computer 29, no. 3 (1996): 31-44.
[24]Hearst, Marti A., Susan T. Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf. "Support vector machines." IEEE Intelligent Systems and their applications 13, no. 4 (1998): 18-28.
[25]Shukla, Praveen Kumar, and Surya Prakash Tripathi. "On the design of interpretable evolutionary fuzzy systems (I-EFS) with improved accuracy." In 2012 International Conference on Computing Sciences, pp. 11-14. IEEE, 2012.
[26]Shukla, Praveen Kumar, and Surya Prakash Tripathi. "Interpretability and accuracy issues in evolutionary multi-objective fuzzy classifiers." International Journal of Soft Computing and Networking 1, no. 1 (2016): 55-69.
[27]Munakata, Toshinori, and Yashvant Jani. "Fuzzy systems: an overview." Communications of the ACM 37, no. 3 (1994): 69-77.
[28]Prasad, R., & Shukla, P. K. Indeterminacy Handling of Adaptive Neuro-fuzzy Inference System Using Neutrosophic Set Theory: A Case Study for the Classification of Diabetes Mellitus.
[29]Prasad, R., Shukla, P.K. (2023). Identification of Ischemic Stroke Origin Using Machine Learning Techniques. In: Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C. (eds) Advances in Data-driven Computing and Intelligent Systems. Lecture Notes in Networks and Systems, vol 653. Springer, Singapore. https://doi.org/10.1007/978-981-99-0981-0_20
[30]Grossman, Robert L., Anil Nerode, Anders P. Ravn, and Hans Rischel, eds. Hybrid systems. Vol. 736. Heidelberg: Springer, 1993.
[31]Rojas, Raul, and Raúl Rojas. "The backpropagation algorithm." Neural networks: a systematic introduction (1996): 149-182.
[32]Oakley, Jeremy, and Anthony O'hagan. "Bayesian inference for the uncertainty distribution of computer model outputs." Biometrika 89, no. 4 (2002): 769-784.
[33]Ambusaidi, Mohammed A., Xiangjian He, Priyadarsi Nanda, and Zhiyuan Tan. "Building an intrusion detection system using a filter-based feature selection algorithm." IEEE transactions on computers 65, no. 10 (2016): 2986-2998.
[34]Hall, Mark A. "Correlation-based feature selection for machine learning." PhD diss., The University of Waikato, 1999.
[35]Hoque, Nazrul, Dhruba K. Bhattacharyya, and Jugal K. Kalita. "MIFS-ND: A mutual information-based feature selection method." Expert Systems with Applications 41, no. 14 (2014): 6371-6385.
[36]Haryanto, Ardy Wibowo, and Edy Kholid Mawardi. "Influence of word normalization and chi-squared feature selection on support vector machine (svm) text classification." In 2018 International seminar on application for technology of information and communication, pp. 229-233. IEEE, 2018.
[37]Pedrycz, Witold. "Why triangular membership functions?." Fuzzy sets and Systems 64, no. 1 (1994): 21-30.
[38]Barua, Aditi, Lalitha Snigdha Mudunuri, and Olga Kosheleva. "Why trapezoidal and triangular membership functions work so well: Towards a theoretical explanation." (2013).
[39]Ali, Omar Adil M., Aous Y. Ali, and Balasem Salem Sumait. "Comparison between the effects of different types of membership functions on fuzzy logic controller performance." International Journal 76 (2015): 76-83.
[40]Wu, Shiqian, and Meng Joo Er. "Dynamic fuzzy neural networks-a novel approach to function approximation." IEEE transactions on systems, man, and cybernetics, part B (cybernetics) 30, no. 2 (2000): 358-364.
[41]García, Salvador, Alberto Fernández, Julián Luengo, and Francisco Herrera. "A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability." Soft Computing 13 (2009): 959-977.
[42]Zhou, Shang-Ming, and John Q. Gan. "Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling." Fuzzy sets and systems 159, no. 23 (2008): 3091-3131.
[43]Prasad, R., Shukla, P.K. (2022). A Review on the Hybridization of Fuzzy Systems and Machine Learning Techniques. In: Bansal, J.C., Engelbrecht, A., Shukla, P.K. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-8225-4_32.