Department of Information Systems, Community College, King Khalid University, Abha, Saudi Arabia.
Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India.
Comput Intell Neurosci. 2022 Jul 9;2022:8491753. doi: 10.1155/2022/8491753. eCollection 2022.
Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scientific articles. The work presented herein attempted to utilize the potential of a unified gaming test of subjects (dyslexia/controls) in tandem with principal components derived from data to detect dyslexia. The work aims to build a machine learning model for dyslexia detection using comprehensive gaming test data. We have attempted to explore the potential of various kernel functions of the Support Vector Machine (SVM) on different number of principal components to reduce the computational complexity. A detection accuracy of 92% is obtained from the radial basis function with 5 components, and the highest detection accuracy obtained from the radial basis function with 3 components is 93%. On the contrary, the Artificial Neural Network(ANN) shows an added advantage with minimal number of hyperparameters with 3 components for obtaining an accuracy of 95%. The comparison of the proposed method with some of the existing works shows efficacy of this method for dyslexia detection.
阅读障碍是儿童中最常见的神经发育障碍之一。因此，检测阅读障碍仍然是各个领域研究工作的重要目标，这一点在各种科学文章中呈现的大量工作中得到了体现。本文旨在利用受测者（阅读障碍/对照组）的统一游戏测试和数据中提取的主成分的潜力来检测阅读障碍。该工作旨在使用综合游戏测试数据构建用于检测阅读障碍的机器学习模型。我们尝试探索了支持向量机（SVM）的各种核函数在不同数量的主成分上的潜力，以降低计算复杂度。使用 5 个主成分的径向基函数可获得 92%的检测准确率，而使用 3 个主成分的径向基函数获得的最高检测准确率为 93%。相反，人工神经网络（ANN）显示了一个额外的优势，即使用 3 个主成分的最小数量的超参数即可获得 95%的准确率。与一些现有工作的比较表明，该方法对阅读障碍检测的有效性。
