Leveraging of recurrent neural networks architectures and SMOTE for dyslexia prediction optimization in children

Dyslexia in children are serious problems that need to be addressed early. Many previous studies have focused on the detection/prediction of dyslexia. However, in the prediction process, there is often an imbalance in the dataset used (between patients with dyslexia and non-dyslexia). Therefore, we are trying to build a system using recurrent neural networks architectures that can quickly and accurately predict the possibility of a child having dyslexia. To overcome the data imbalance between dyslexics and non-dyslexics, we also apply the synthetic minority oversampling technique (SMOTE) method to the dataset. SMOTE will synthesize dyslexic data to balance the numbers with non-dyslexic data. This study used a dataset of 3640 participants (392 dyslexic and 3248 non-dyslexics). For the process of predicting dyslexia, several algorithms such as simple recurrent neural networks (RNN), long short term-memory (LSTM), and gate recurrent units (GRU) are used. As a result, there is an increase in prediction accuracy when SMOTE is applied (compared to without SMOTE) in the dyslexia forecasting process using RNN (92.68% for training and 91.16% for testing), LSTM (94.81% for training and 93.16% for testing), and GRU (96.43% for training and 92.24% for testing). Using SMOTE+RNN architecture in this research increased the accuracy of dyslexia prediction by up to 5% compared to without SMOTE.