Applying Bagging Machine Learning Techniques to Diagnose Dyslexia in Visual Tasks
- سال انتشار: 1403
- محل انتشار: هشتمین کنفرانس ملی پژوهشهای کاربردی در مهندسی برق، مکانیک و مکاترونیک
- کد COI اختصاصی: ELEMECHCONF08_097
- زبان مقاله: انگلیسی
- تعداد مشاهده: 218
نویسندگان
Department of Biomedical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Biomedical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Psychology, University of Tehran, Tehran, Iran
چکیده
Dyslexia is a disorder of neurological origin that primarily impacts children's learning, resulting in difficulties with reading and writing. If left undiagnosed, it can cause significant frustration and feelings of intimidation for both the affected children and their families. Without early intervention, these children may experience substantial academic achievement gaps by the time they reach high school. Early detection and intervention for dyslexic students are crucial for fostering positive self-esteem and maximizing academic potential. This paper introduces a Bagging approach for automatically identifying dyslexia in children using machine learning techniques. In this study, we pre-processed brain signals and extracted EEG signal features across ۱۹ channels, focusing on the amplitude and latency of ERP components. Due to the high number of features, we employed Principal Component Analysis (PCA) for feature reduction. To prevent overfitting, we utilized K-fold cross-validation and ultimately, we applied the bagging method for classification.Using this approach, we achieved an overall average classification accuracy of ۹۰.۶%, with sensitivity and specificity rates of ۱۰۰% and ۸۱.۲%, respectivelyکلیدواژه ها
_Dyslexia, Electroencephalography (EEG), Ensemble Learning, Machine Learning, Event-Related Potential (ERP)_اطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.