MQAD-net: multi-modal quantum-attentive deep learning framework for early mental health detection and personalized therapy recommendation

Mental health issues are a major global concern that not only affect millions of people but also create significant societal and economic costs. Traditional ways of diagnosing mental health problems are usually based on the opinions of experts, thus prolonging the process of getting a diagnosis, causing inconsistencies, and even sometimes leading to the wrong conclusion. The already available computational models are not fully equipped to cope with the problem of redundancy of features, complicated through capturing interdependencies between various types of data, and the issue of having to really scale them for actual use cases of the world. The paper introduces the MQAD-Net (Multi-Modal Quantum-Attentive Deep Learning Network), an advanced framework for mental health prediction and personalized therapy recommendation, as a solution. The suggested method uses GAT for temporal-spatial EEG feature extraction and transformer-based embeddings for behavioral text analysis to combine various kinds of data such as EEG signals, voice patterns, and behavioral text responses through Graph Attention Networks (GAT). Feature selection is being optimized through Quantum Greylag Multi-Criteria Decision-Making Feature Selection (QGMFS) which is a combination of Quantum-Based Particle Swarm Optimization (QPSO), Grey Wolf Optimization (GWO), and Multi-Criteria Decision Making (MCDM) that assists in choosing the most informative and non-redundant features. Dense-DualLSTMNet (DDL-Net) classification is conducted, which innovatively integrates three methodologies, namely, DenseNet, DPN-68, and BiLSTM, for better multi-modal feature learning and sequential modeling. The outcome of the experimental evaluation shows that MQAD-Net significantly exceeds the traditional deep learning models, achieving an accuracy of 95%, a precision of 94%, a recall of 93%, and an F1-score of 94%, which also allows it to recommend personalized therapy. These results highlight the potential of the framework to enhance the early diagnosis of mental health conditions, to facilitate the treatment planning for each individual, and to provide support for clinical decision-making in healthcare settings that are real-world.