Collaborative Cross-Modal Fusion with Conditional GAN Augmentation for Mental Disorder Risk Prediction using Deep Learning

Abstract

Mental health disorders are frequently under-diagnosed because clinical decisions rely on isolated data sources such as structured screening scores or unstructured self-reported narratives, rarely both together. This paper proposes a multimodal mental health prediction framework that jointly models structured clinical and lifestyle attributes (e.g., PHQ-9 and GAD-7 scores, sleep duration, physical activity, demographic variables) together with unstructured free-text self-statements collected from the same individual. The proposed architecture combines a collaborative cross-modal learning module, which aligns tabular and textual representations in a shared latent space via co-attention, with a conditional Generative Adversarial Network (GAN) that synthesizes minority-class embeddings to counter the class imbalance typical of mental health datasets. A simulated multimodal cohort of 5,000 respondents, each contributing paired structured responses and a free-text statement, is used to train and evaluate the framework. Experimental results show that the proposed Collaborative-GAN model achieves 91.4% accuracy and an AUC-ROC of 0.95, outperforming unimodal and naive late-fusion baselines by 4 to 9 percentage points across all metrics. These findings suggest that collaborative multimodal fusion combined with adversarial data augmentation can meaningfully improve early mental health risk screening, and the framework is presented as a reproducible template that researchers can adapt to real clinical datasets.