Q-MIND: enhancing ADHD diagnosis using quantum machine learning for advanced neuroimaging analysis.

Attention deficit hyperactivity disease (ADHD) is a neurobehavioural disorder of heterogeneous nature that is common among children and adolescents. It is marked by symptoms such as inattention, hyperactivity, and impulsivity that substantially impact an individual's everyday living and well-being. Due to the complicated nature of the disorder, it is challenging to obtain an accurate and timely diagnosis despite its prevalence. The lack of standardized testing techniques further deepens the problem. As a result, the number of undiagnosed cases is alarmingly high. This research presents a data-driven approach to enhance clinical decision-making in ADHD diagnosis using quantum machine learning and evolutionary algorithms. Leveraging the publicly available ADHD-200 dataset, which contains neuroimaging and associated phenotypic data from multiple global research sites, a comprehensive diagnostic framework is proposed. Feature extraction is carried out using a Quantum Convolutional Neural Network (QCNN), designed to identify nuanced patterns within high-dimensional MRI and behavioral data. To further refine the dataset, a novel Differential Evolution-Swarm Optimization (DE-Swarm) algorithm is introduced, combining the strengths of Differential Evolution and Particle Swarm Optimization for effective feature selection. This algorithm ensures minimal redundancy while improving interpretability and model robustness. The selected features are then fed into an AutoML system for model selection and hyperparameter optimization. Among several models tested, the Gradient Boosting Classifier achieved the highest test accuracy of 98.53%, outperforming existing techniques in terms of precision, recall, and specificity. By integrating quantum computing principles with optimized evolutionary strategies, this study contributes a robust and scalable framework for ADHD diagnosis. The work demonstrates how the integration of phenotypic and neuroimaging data, when processed through advanced machine learning pipelines, can significantly enhance diagnostic precision and support more personalized clinical assessments.