Abstract: Mental health disorders are becoming increasingly prevalent worldwide, and early identification of psychological risk factors remains a major challenge. Traditional clinical assessments are periodic and rely heavily on self-reporting, which may delay timely intervention. There is a growing need for intelligent systems capable of continuous monitoring while maintaining patient privacy.

NeuroGuard proposes a multimodal artificial intelligence framework designed to act as a secure intermediary between patients and healthcare professionals. The system collects consent-based behavioral, textual, and emotional data through a mobile application and processes it locally to detect early signs of mental health deterioration.

The framework integrates transformer-based natural language processing models, behavioral sequence analysis, and multimodal feature fusion to generate structured mental health risk scores. To preserve privacy, federated learning is employed so that raw patient data remains on the device while only encrypted model parameters are shared with the central aggregation server.

An explainable AI module ensures transparency by highlighting contributing factors behind risk predictions, allowing doctors to understand clinical indicators without accessing sensitive personal data. The system provides summarized risk insights rather than full conversations or raw behavioral logs.

Additionally, NeuroGuard includes an AI-powered recommendation and chatbot module that offers personalized coping strategies, educational guidance, and preventive interventions. By combining privacy preservation, explainability, and proactive risk assessment, the proposed framework presents a scalable and ethical solution for early mental health risk prediction and intervention.

Keywords: Mental Health Monitoring, Multimodal Deep Learning, Federated Learning, Explainable Artificial Intelligence (XAI), Risk Prediction, Early Intervention, Doctor–Patient Intermediary System, Privacy-Preserving AI, Transformer-Based NLP, Healthcare Chatbot.

Downloads: | DOI: 10.17148/IARJSET.2026.13349