# Deep Learning Applications for Dyslexia Prediction
###### Abstract
The literature has demonstrated the success of ML methods in classification problems, particularly the classification of diseases. Moreover, ML methods have been found to have outstanding accuracy for diagnosing dyslexia [12]. However, traditional ML methods fail to use raw data to implement these tasks [13]. To overcome this obstacle, ML models have used a layered learning approach, known as the DL. The general difference between DL models and traditional ML models is that DL models do not require the engineering step of feature extraction, which is inherent to conventional models, as they automatically learn abstract hierarchical feature  
Figure 1: This image shows that any disorder in the brain’s left hemisphere would lead to important difficulties in a person’s ability to read and write and some other skills. representations from data [14]. ML might be utilized to accomplish specific tasks, for instance, the identification of objects in images, speech-to-text transformation, and isolation of items in categories [13]. DL utilizes ANNs that simulate the brain's function. Since ANNs were introduced, they have undergone considerable alteration and development; however, the basic principle has remained the same [13]. The ANN structure essentially comprises nodes and edges that, together, create a structure that resembles a neuron and are in charge of transmitting the information [13]. It consists of three strata: input strata, several hidden strata, and output strata. In each layer, every node is linked to every other node in the next layer. The network becomes deeper as the number of hidden layers increases, leading to a DNN model. A DNN converts raw inputs to helpful features. Subsequent layers elicit a group of features that are considered more abstract and help to achieve a desired task. The essential processes in DL are feature extraction and selection [13]. DNNs have various architectures. A basic one is a feed-forward network, in which the structure has links among layers in a single direction (forward), and there are no existing loops or cycles in the whole structure. Other architectures include particular functions, such as RNNs, which are implemented with sequential datasets, whereas CNNs are implemented with grid-structured data-like images. MLP and CNN models have been applied at different times and places to reveal and classify dyslexia utilizing special data related to dyslexia children. This review paper focuses on the use of advanced ML methods (DL) to help in diagnosing dyslexia disorder and constructing a predictive model for prediction. The following points are investigated:  
* The types of datasets that are used by prediction models. * Different DL models that are utilized to predict dyslexia disorder. * The performance of DL models in dyslexia prediction. To the best of our knowledge, this is the first review to investigate current works that specifically utilized DL models to diagnose dyslexia. This will allow us to investigate the points above, seeking to find a new way to predict dyslexia. Previous reviews on dyslexia prediction have focused on ML models in general, not on DL models. Following this initial introduction, Section 2 provides an overview of related works, both review papers and systematic reviews, and illustrates the difference between previous reviews and the current work. Section 3 discusses the search strategy and explains the stages of the article selection process, including the criteria. Section 4 provides a detailed explanation of every article in the results section, including information on dataset acquisition, dataset preprocessing, feature selection, and model prediction performance. Section 5 lists and discusses some challenges related to scanning the articles, and Section 6 presents concludes the paper.