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PsycheGuard AI: An Intelligent Platform for Early Detection of Mental Health Disorders Through Multimodal Emotion Analysis
Rohit M S, Srinivas R, Yogesh B
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Abstract: Depression, anxiety, and chronic stress are now widespread, yet timely diagnosis remains difficult to achieve at scale. A shortage of trained clinicians, the stigma still attached to seeking help, and the absence of any ongoing, objective way to track a person's psychological state all contribute to delayed care and poorer long-term outcomes. Most current mental-health apps rely on static questionnaires or basic sentiment scoring, neither of which captures the nuance needed to flag a developing problem early or tailor support to the individual. PsycheGuard AI is put forward here as a web-based monitoring system that draws on machine learning and natural language processing to close this gap. It reads a user's written and spoken input, behavioral cues, and standard screening results to surface early indicators of depression, anxiety, and stress, then translates that analysis into a running risk level, a mood trend over time, and tailored recommendations, with alerts raised when intervention appears warranted. The implementation uses Python and Django on the backend, a Bootstrap-based front end, and Scikit-learn together with NLTK/spaCy for the modelling layer, backed by SQLite/MySQL for storage and Matplotlib/Plotly for visual reporting. Testing shows the combined system outperforms single-method screening approaches while sustaining continuous mood tracking and dependable emotion classification. Authentication, encryption at rest, and role-based permissions protect user data throughout. Because the architecture stays computationally light, the platform is realistic to deploy at scale in settings such as universities and workplaces.
Keywords: Mental Health AI, Early Disorder Detection, Emotion Recognition, Depression Detection, Anxiety Classification, Multimodal Fusion, MentalBERT, Federated Learning, Explainable AI, Temporal Convolutional Network, Digital Mental Health, Predictive Psychological Care
Keywords: Mental Health AI, Early Disorder Detection, Emotion Recognition, Depression Detection, Anxiety Classification, Multimodal Fusion, MentalBERT, Federated Learning, Explainable AI, Temporal Convolutional Network, Digital Mental Health, Predictive Psychological Care
How to Cite:
[1] Rohit M S, Srinivas R, Yogesh B, βPsycheGuard AI: An Intelligent Platform for Early Detection of Mental Health Disorders Through Multimodal Emotion Analysis,β International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.156115
