A Machine Learning Driven Serious Gaming Framework for Predicting Dyslexia

Abstract

"This study investigated the efficacy of a Feedforward Neural Network (FNN) Multi-Layer Perceptron (MLP) in predicting dyslexia in children. The MLP model achieved a notable accuracy of 78%, representing a significant improvement compared to the baseline MLP model without transfer learning. The utilization of transfer learning was pivotal in enhancing the predictive capacity of the model. Transfer learning involves leveraging pre-existing knowledge from broader datasets and adapting it to the specific task at hand. In this case, incorporating transfer learning methodologies allowed the model to better understand and predict dyslexia indicators in children. The study also highlights the importance of early detection and intervention in managing learning disorders such as dyslexia. By accurately predicting dyslexia at an early age, interventions and support can be initiated promptly, potentially mitigating the long-term effects of the disorder on a child's academic and personal development. Furthermore, the integration of a language-independent game framework offers a non-invasive and engaging means of screening for dyslexia symptoms. By analyzing gameplay data alongside traditional assessment measures, a comprehensive predictive model is formulated, enhancing the accuracy and reliability of dyslexia detection. Overall, these findings underscore the potential of neural network models, particularly when augmented with transfer learning techniques, in facilitating early detection and intervention for learning disorders in children."