Machine and Deep Learning Algorithms for ADHD Detection: A Review

Hernández-Capistran, Jonathan; Sánchez-Morales, Laura Nely; Alor-Hernández, Giner; Bustos-López, Maritza; Sánchez-Cervantes, José Luis

doi:10.1007/978-3-031-40688-1_8

Cited by 5 publications

(1 citation statement)

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“…Moreover, the reliance on extensive computational resources for data processing and model training limits the accessibility of DNN methodologies, particularly in resource-constrained research environments. The demand for vast, meticulously annotated data sets further complicates research efforts (Hernández-Capistran et al, 2023 ), given the inherent variability in ADHD manifestations across individuals and the ethical considerations tied to patient data privacy.…”

Section: Related Workmentioning

confidence: 99%

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD

Yu,

Fang

2024

Front. Neurosci.

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Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, and impulsivity. Current research in this field faces challenges, particularly in accurate diagnosis and effective treatment strategies. The analysis of motor information, enriched by artificial intelligence methodologies, plays a vital role in deepening our understanding and improving the management of ADHD. The integration of AI techniques, such as machine learning and data analysis, into the study of ADHD-related motor behaviors, allows for a more nuanced understanding of the disorder. This approach facilitates the identification of patterns and anomalies in motor activity that are often characteristic of ADHD, thereby contributing to more precise diagnostics and tailored treatment strategies. Our approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies. On the ADHD dataset, the model significantly improved accuracy to 98.21% and recall to 93.86%, especially excelling in EEG data processing with accuracy and recall rates of 96.62 and 95.21%, respectively, demonstrating precise capturing of ADHD characteristic behaviors and physiological responses. These results not only reveal the great potential of our model in improving ADHD diagnostic accuracy and developing personalized treatment plans, but also open up new research perspectives for understanding the complex neurological logic of ADHD. In addition, our study not only suggests innovative perspectives and approaches for ADHD treatment, but also provides a solid foundation for future research exploring similar complex neurological disorders, providing valuable data and insights. This is scientifically important for improving treatment outcomes and patients' quality of life, and points the way for future-oriented medical research and clinical practice.

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Section: Related Workmentioning

confidence: 99%