Attention-deficit/hyperactivity disorder detection leveraging an ensemble of encoder-decoder transformer and XGBoost models

Abstract

"Early detection of neurodevelopmental disorders such as Attention-Deficit/ Hyperactivity Disorder(ADHD), can lead to improved outcomes and prompt intervention. Traditional detection methods have been facing challenges due to judgment and misinterpretations, lack of resources, and biasness which may cause under-diagnosing or over-diagnosing. Early detection of these neurodevelopmental disorders, not only helps individuals to get proper ministrations but also it can improve their social, cognitive and mental development. In this study, our aim is to build an ensemble model leveraging a custom Transformer with various attention mechanisms alongside an XGBoost model to improve diagnostic accuracy. By comparing the proposed model with other traditional machine learning and deep learning models, this study aims to enhance the accuracy and efficiency of diagnosis. By using a pre-processed EEG dataset and customized ensemble model, the proposed model has achieved 83% of accuracy, highest accuracy among the traditional models. Moreover, this research aims for future development in the field, by offering methodologies that can be useful to further studies focused on disorder detection. In conclusion, this research will use an ensemble model leveraging a custom Transformer with various attention mechanisms alongside an XGBoost model for early ADHD aiming to create a new precision and accessibility in identifying neurodevelopmental disorders. "