Abstract
The application of Deep Learning (DL) and Machine Learning (ML) methodologies to the investigation and management of Attention Deficit Hyperactivity Disorder (ADHD) represents a significant advancement with broad clinical and research implications. This systematic review analyzes various machine learning and deep learning algorithms, such as Support Vector Machines, k-Nearest Neighbors, Decision Trees, Random Forests, and Convolutional Neural Networks, and their applicability to ADHD research and clinical practice. These computational methods have shown significant usefulness in various areas, including improving diagnostic precision, recognizing different subtypes of ADHD, forecasting treatment outcomes, and revealing neurobiological markers linked to the condition. Utilizing a variety of data sources, such as clinical evaluations, neuroimaging results, genetic data, and environmental influences, machine learning and deep learning methods play a significant role in clarifying the diversity and fundamental mechanisms of ADHD. Moreover, integrating multi-modal data, longitudinal studies following individuals over time, real-time surveillance systems, and interdisciplinary collaboration are all potential future directions for ML and DL in ADHD research. Furthermore, future research pathways consist of the integration of multimodal datasets, longitudinal analyses, real-time monitoring systems, and strengthened interdisciplinary collaboration. Addressing ethical considerations and ensuring equitable application will be critical for the successful translation of these technologies into routine clinical practice. Overall, ML and DL hold considerable promise for transforming ADHD evaluation and treatment, ushering in a new era of personalized and effective interventions personalized to individual needs
