AI-Enhanced Depression Detection System: Integrating Multimodal Analysis for Early Diagnosis

Depression is a prevalent mental health condition affecting millions globally, often going undiagnosed due to social stigma and limited accessibility to healthcare. This study presents an innovative AI-enhanced system for early depression detection, utilizing multimodal analysis integrating natural language processing (NLP) and patient health metrics. Two machine learning models form the system's core: a text classification model leveraging logistic regression to analyze user inputs and predict emotional tone and a health-data-based model that incorporates factors such as BMI, blood features, and comorbid conditions for comprehensive evaluation. The models were integrated into a web application offering a dual advantage of accurate screening and accessible mental health recommendations. Results demonstrated high efficacy with classification accuracy exceeding 91% across models, underscoring its potential to transform early-stage mental health interventions. Future enhancements include adopting advanced deep learning frameworks and expanding multilingual capabilities to broaden accessibility.