Major Depressive Disorder Detection using Dung Beetle Optimization based QGNN Classification

Major Depressive Disorder (MDD) is prevalent and incapacitating mental health disorder which mandates correct and early diagnosis for appropriate intervention. Conventional diagnostic methods are based on subjective assessments, often resulting in discrepancies and delayed treatment. Into our research, we offer novel framework based upon Quantum Graph Neural Networks (QGNN) for automatic detection of MDD, which focuses on leveraging deep learning and optimization techniques to improve accuracy in the diagnostic process. The DLC integrates QGNN with Dynamic Bayesian Optimization (DBO) for hyperparameter fine-tuning, enhancing model's predictive accuracy. Further, enhanced data preprocessing methods are applied, like Hanning Window function, for filtering out noise and enhancing input features. Experimental evaluations on the MDD patient dataset demonstrate that proposed approach achieves an accuracy of 86.62%, higher than existing graph-based models. Quantum-Inspired Schemes for Graph Learning and Optimization Techniques for NMPDD Classification and Recognition Expanding datasets, optimizing deep learning architectures, and incorporating clinical data are potential avenues for future research for further improving model generalization and diagnostic reliability. These conclusions specify a QGNN-DBO has probability to be powerful, scalable, and efficient solution for automatic MDD detection, potentially enhancing the landscape of mental health diagnostics and treatment planning