|تعداد مشاهده مقاله||2,477,329|
|تعداد دریافت فایل اصل مقاله||1,746,045|
|Journal of Electrical and Computer Engineering Innovations (JECEI)|
|مقاله 17، دوره 10، شماره 1، فروردین 2022، صفحه 209-220 اصل مقاله (1.39 M)|
|نوع مقاله: Original Research Paper|
|شناسه دیجیتال (DOI): 10.22061/jecei.2021.8109.480|
|K. Ali Mohsin Alhameedawi1؛ R. Asgarnezhad* 2|
|1Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran Department of Computer Engineering, Al-Rafidain University of Baghdad, Baghdad, Iraq|
|2Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran and Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Technical and Vocation University (TVU), Tehran, Iran|
|تاریخ دریافت: 04 خرداد 1400، تاریخ بازنگری: 25 شهریور 1400، تاریخ پذیرش: 02 مهر 1400|
|Background and Objectives: Autism is the most well-known disease that occurs in any age people. There is an increasing concern in appealing machine learning techniques to diagnose these incurable conditions. But, the poor quality of most datasets contains the production of efficient models for the forecast of autism. The lack of suitable pre-processing methods outlines inaccurate and unstable results. For diagnosing the disease, the techniques handled to improve the classification performance yielded better results, and other computerized technologies were applied.|
Methods: An effective and high performance model was introduced to address pre-processing problems such as missing values and outliers. Several based classifiers applied on a well-known autism data set in the classification stage. Among many alternatives, we remarked that combine replacement with the mean and improvement selection with Random Forest and Decision Tree technologies provide our obtained highest results.
Results: The best-obtained accuracy, precision, recall, and F-Measure values of the MVO-Autism suggested model were the same, and equal 100% outperforms their counterparts.
Conclusion: The obtained results reveal that the suggested model can increase classification performance in terms of evaluation metrics. The results are evidence that the MVO-Autism model outperforms its counterparts. The reason is that this model overcomes both problems.
|Data Mining؛ Pre-processing؛ Machine Learning Techniques؛ Autism Mellitus|
 M.L. Pennington, D. Cullinan, L.B. Southern, "Defining autism: variability in state education agency definitions of and evaluations for autism spectrum disorders," Autism Res. Treat., 2014: 1-8, 2018.
 A. Roman-Urrestarazu, C. Yáñez, C. López-Garí, C. Elgueta, C. Allison, C. Brayne, et al., "Autism screening and conditional cash transfers in Chile: Using the Quantitative Checklist (Q-CHAT) for early autism detection in a low resource setting," Autism, 25: 932-945, 2021.
 G. Divan, S. Bhavnani, K. Leadbitter, C. Ellis, J. Dasgupta, A. Abubakar, et al., "Annual Research Review: Achieving universal health coverage for young children with autism spectrum disorder in low‐and middle‐income countries: A review of reviews," J. Child Psychol. Psychiatry, 62: 514-535, 2021.
 R. Asgarnezhad, A. Monadjemi, M. Soltanaghaei, "NSE-PSO: Toward an effective model using optimization algorithm and sampling methods for text classification," J. Electr. Comput. Eng. Innovations 8(2): 183-192, 2020.
 L. Zhang, A.Z. Amat, H.Zhao, A. Swanson, A.S. Weitlauf, Z. Warren, et al., "Design of an intelligent agent to measure collaboration and verbal-communication skills of children with autism spectrum disorder in collaborative puzzle games," IEEE Trans. Learn. Technol., 14(3): 338-352, 2020.
 S. Kopp, L. Gesellensetter, N.C. Krämer, I. Wachsmuth, "A conversational agent as museum guide–design and evaluation of a real-world application," in Proc. International Workshop on Intelligent Virtual Agents: 329-343, 2005.
 R. Yaghoubzadeh, K. Pitsch, S. Kopp, "Adaptive grounding and dialogue management for autonomous conversational assistants for elderly users," in Proc. International Conference on Intelligent Virtual Agents: 28-38, 2015.
 C.-H. Min, "Automatic detection and labeling of self-stimulatory behavioral patterns in children with Autism Spectrum Disorder," in Proc. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 279-282, 2017.
 T. Westeyn, K. Vadas, X. Bian, T. Starner, G.D. Abowd, "Recognizing mimicked autistic self-stimulatory behaviors using hmms," in Proc. Ninth IEEE International Symposium on Wearable Computers (ISWC'05): 164-167, 2005.
 E. Linstead, R. German, D. Dixon, D. Granpeesheh, M. Novack, A. Powell, "An application of neural networks to predicting mastery of learning outcomes in the treatment of autism spectrum disorder," in Proc. 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA): 414-418, 2015.
 O. Altay, M. Ulas, "Prediction of the autism spectrum disorder diagnosis with linear discriminant analysis classifier and K-nearest neighbor in children," in Proc. 2018 6th International Symposium on Digital Forensic and Security (ISDFS): 1-4, 2018.
 M.J. Maenner, M. Yeargin-Allsopp, K. Van Naarden Braun, D.L. Christensen, L.A. Schieve, "Development of a machine learning algorithm for the surveillance of autism spectrum disorder," PloS one, 11(12): 1-11,, 2016.
 I.N. Yulita, M.I. Fanany, A.M. Arymurthy, "Comparing classification via regression and random committee for automatic sleep stage classification in autism patients," J. Phys. Conf. Ser., 1230: 1-9, 2019.
 R. Anirudh, J.J. Thiagarajan, "Bootstrapping graph convolutional neural networks for autism spectrum disorder classification," in Proc. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): 3197-3201, 2019.
 Y. Kong, J. Gao, Y. Xu, Y. Pan, J. Wang, J. Liu, "Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier," Neurocomputing, 324: 63-68, 2019.
 Z. Sherkatghanad, M. Akhondzadeh, S. Salari, M. Zomorodi-Moghadam, M. Abdar, U.R. Acharya, et al., "Automated detection of autism spectrum disorder using a convolutional neural network," Front. Neurosci., 13: 13-25, 2020.
 R. Asgarnezhad, S.A. Monadjemi, M. Soltanaghaei, "A new hierarchy framework for feature engineering through multi-objective evolutionary algorithm in text classification," Concurrency Comput. Pract. Ex., 2021.
تعداد مشاهده مقاله: 1,623
تعداد دریافت فایل اصل مقاله: 390