تشخیص دقیق و زودهنگام کودکان پیش دبستانی مستعد نارساخوانی (دیسلکسیا): مقایسه دو سیستم هوشمند طراحی شده با شبکه عصبی مصنوعی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 محقق فوق دکترا، دکترای روان‌شناسی کودکان استثنایی، دانشکده روان‌شناسی و علوم تربیتی، دانشگاه تهران، تهران، ایران

2 استاد ممتاز، دانشکده روان شناسی و علوم تربیتی، دانشگاه تهران، تهران، ایران

چکیده

مقدمه و اهداف
هدف از پژوهش حاضر شناسایی دقیق و زودهنگام کودکان پیش‌دبستانی مستعد دیسلکسیا از طریق طراحی سیستم هوشمند کمک تشخیصی بود.
مواد و روش ­ها
پژوهش حاضر از نظر هدف از نوع مطالعات تحقیق و توسعه‌ای و از نظر شیوه جمع‌آوری داده‌، توصیفی، پیمایشی از نوع ارزیابی و تشخیص بود. ابزاری که جهت ارزیابی و استخراج اطلاعات در پژوهش حاضر مورد استفاده قرار گرفت برنامه عصبی-شناختی طراحی شده توسط دلاوریان و همکاران بود که کارایی، دقت، روایی و اعتبار آن در بسیاری از مطالعات پیشین اذعان و به اثبات رسیده است. نمونه‌ها به روش تصادفی چند مرحله‌ای خوشه‌ای انتخاب شدند و عملکرد هر یک از آنها به مدت دو سال، تا زمان تشخیص قطعی، در فایل‌های اکسلِ جداگانه ذخیره شد و پس از دو سال جهت طراحی سامانه حمایتگر تصمیم بالینی مورد استفاده قرار گرفت. دو نوع شبکه عصبی مصنوعی چند لایه پرسپترون و تابع پایه شعاعی در طراحی سامانه، استفاده و از نظر دقت و حساسیت مقایسه شدند.
یافته­ ها
میانگین دقت سامانه‌های طراحی شده توسط شبکه عصبی چند لایه پرسپترون، 40/94% و حساسیت و اختصاصی بودن شبکه در تشخیص کودکان پیش‌دبستانی مستعد دیسلکسیا به ترتیب، 27/90% و 28/95‌% حاصل گردید.
نتیجه­ گیری
با توجه به اعتبار و روایی بالای برنامه عصبی‌- شناختی و دقت و حساسیت بالای سامانه حمایتگر تصمیمِ طراحی شده، می‌توان با اطمینان بالا از این سامانه هوشمند جهت تشخیص زودهنگام کودکان مستعد دیسلکسیا، پیش از ورود به دبستان استفاده نمود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Accurate and Early Diagnosis of Children at Risk for Dyslexia: Comparison of Two Intelligent Systems Designed by Artificial Neural Network

نویسندگان [English]

  • Mona Delavarian 1
  • Gholamali Afrooz 2
1 Department of Psychology and Education, University of Tehran, Tehran, Iran
2 Department of Psychology and Education, University of Tehran, Tehran, Iran
چکیده [English]

Background and Aim: The aim of the present study was early and accurate diagnosis of preschoolers at risk for dyslexia through designing an intelligent diagnostic system.
Materials and Methods: The current research was a “research and development” type of investigation, in terms of its goal, and a descriptive research, assessment, and diagnostic type study, in terms of data collection method. The Neuro-cognitive program designed by Delavarian et al. was used for evaluation of the children. The efficacy, accuracy, validity, and reliability of this program were proven in many previous studies. Participants were selected following cluster random sampling method and their neuro-cognitive functions were saved for two years until the definite diagnosis of each individual was determined and then the data was applied in designing the diagnostic system. Multilayer perceptron and radial basis function artificial neural networks were applied in designing the system and they were compared according to their accuracy and sensitivity.
Results: The average accuracy of the system in early diagnosis of preschoolers at risk for dyslexia, designed by multilayer perceptron neural network, reached to 94.40% and the network’s sensitivity and specificity were obtained to be 90.27 and 95.28%, respectively.
Conclusion: According to the high validity and reliability of the neuro-cognitive program and the high accuracy and sensitivity of the designed decision support system, the mentioned system could be used in early detection of at risk preschoolers, prior to entering the elementary school.

کلیدواژه‌ها [English]

  • Intelligent Diagnostic System
  • Multi-layer Perceptron Artificial Neural Network
  • Radial Basis Function Artificial Neural Network
  • Children at Risk for Dyslexia
  • Neuro-Cognitive Program
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