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Ali Mohsin Alhameedawi, K., Asgarnezhad, R.. (1400). MVO-Autism: An Effective Pre-treatment with High Performance for Improving Diagnosis of Autism Mellitus. فناوری آموزش, 10(1), 209-220. doi: 10.22061/jecei.2021.8109.480
K. Ali Mohsin Alhameedawi; R. Asgarnezhad. "MVO-Autism: An Effective Pre-treatment with High Performance for Improving Diagnosis of Autism Mellitus". فناوری آموزش, 10, 1, 1400, 209-220. doi: 10.22061/jecei.2021.8109.480
Ali Mohsin Alhameedawi, K., Asgarnezhad, R.. (1400). 'MVO-Autism: An Effective Pre-treatment with High Performance for Improving Diagnosis of Autism Mellitus', فناوری آموزش, 10(1), pp. 209-220. doi: 10.22061/jecei.2021.8109.480
Ali Mohsin Alhameedawi, K., Asgarnezhad, R.. MVO-Autism: An Effective Pre-treatment with High Performance for Improving Diagnosis of Autism Mellitus. فناوری آموزش, 1400; 10(1): 209-220. doi: 10.22061/jecei.2021.8109.480

MVO-Autism: An Effective Pre-treatment with High Performance for Improving Diagnosis of Autism Mellitus

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
نویسندگان
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 
چکیده
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.
کلیدواژه‌ها
Data Mining؛ Pre-processing؛ Machine Learning Techniques؛ Autism Mellitus
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