AlGhamdi Ahmed Saeed
Department of Computer Engineering, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21994, Saudi Arabia.
Children (Basel). 2022 Sep 1;9(9):1337. doi: 10.3390/children9091337.
There are a large number of neurological disorders being explored regarding possible management and treatment, with dyslexia being one of the disorders that affect children at the onset of their learning process. Dyslexia is a developmental neurological disorder that prevents children from learning. The disorder has a prevalence of around 10% across the globe, as reported by most of the literature on dyslexia. The early detection and management of dyslexia is one of the primary pursuits among different research. One such domain that leads this pursuit of the early detection and management of dyslexia is artificial intelligence. With so much effort being expended to explore the applicability of artificial intelligence to address the problem of dyslexia detection, in this work, an ensemble model for the early detection of dyslexia is proposed and recommend. The work experimentally considers a pool of ensembles with rigorous validation on a large sized dataset. The final ensemble model recommendation for detection is expressed after evaluating all of the ensemble frameworks based on a number of evaluation parameters. Our experiments reveal that the subspace discriminant ensemble showed superiority for the detection of dyslexia with an accuracy of 90% on five-fold cross validation with the least training time. An accuracy of 90.90% was achieved using boosted trees with a holdout validation of 30%, while with no validation the subspace K-Nearest Neighbor (KNN) outperformed the other ensembles with an accuracy of 99.9%.
目前正在探索大量神经系统疾病的可能管理和治疗方法，诵读困难症是其中一种在儿童学习过程开始时就会影响他们的疾病。诵读困难症是一种发育性神经系统疾病，会阻碍儿童学习。正如大多数关于诵读困难症的文献所报道的那样，这种疾病在全球的患病率约为10%。诵读困难症的早期检测和管理是不同研究的主要追求之一。人工智能是引领这种对诵读困难症早期检测和管理追求的一个领域。在为探索人工智能在解决诵读困难症检测问题方面的适用性投入了大量精力之后，在这项工作中，提出并推荐了一种用于诵读困难症早期检测的集成模型。这项工作通过在一个大型数据集上进行严格验证，对一系列集成模型进行了实验性研究。在根据多个评估参数对所有集成框架进行评估之后，给出了用于检测的最终集成模型推荐。我们的实验表明，子空间判别集成在五折交叉验证中以90%的准确率、最短的训练时间在诵读困难症检测方面表现出优越性。使用留出验证率为30%的提升树实现了90.90%的准确率，而在无验证的情况下，子空间K近邻（KNN）以99.9%的准确率优于其他集成模型。
