Work

A Hybrid Approach to Attention Deficit Hyperactivity Disorder Detection Leveraging Transformer and XGBoost Models Using XSparseFormerNet

Year: 2025

Type: article

Abstract: Early detection of neurodevelopmental disorders such as attention-deficit/hyperactivity disorder (ADHD) is crucial for improved outcomes and prompt intervention. However, traditional detection methods often suffer from challenges due to subjectivity and misinterpretation, lack of resources, and diagnostic biases that can lead to underdiagnosis or overdiagnosis. Early detection of these neurodevelopmental disorders not only helps individuals obtain proper ministrations but can also improve their social, cognitive, and mental development. This study proposes an ensemble model-XSparseFormerNet-that leverages, a custom encoder-decoder Transformer enhanced with various attention mechanisms alongside an XGBoost model to enhance diagnostic accuracy. The proposed model aims to increase the accuracy and efficiency of diagnosis via a custom transformer architecture with the gradient boosting algorithm XGBoost. By using a preprocessed EEG dataset and a customized ensemble model, XSparseFormerNet achieves 85% accuracy, outperforming traditional methods across evaluation metrics. Moreover, this research contributes to future development in the field by offering methodologies that can be useful for studies on disorder detection.

Source: Scientific Reports

DOI: 10.1038/s41598-025-24919-3

Authors: Sharon Rose Sarker, Saowmi Mehjabin, Meherin Majid Piper, Rafeed Rahman, Fahim Ul Islam, Md. Golam Rabiul Alam

Institution: BRAC University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690