Abstract: Digitization speed in our current era shows mental health problems growing as a vital dilemma which particularly affects workers in demanding professional roles. The AI-Driven Mental Health Diagnosis platform performs mental health condition predictions through advanced machine learning algorithms which process user inputs as well as analyse behavioural patterns together with historical data. The system creates comprehensive mental well-being assessments through its ability to evaluate formatted data and free-form information about symptoms with added lifestyle conditions and workplace stress elements. The platform achieves this through exploratory data analysis methods which both reveal dominant patterns and danger elements behind mental health deteriorations.
The predictive system uses multiple machine learning approaches which combine logistic regression with decision trees and random forests and neural networks for mental health condition diagnosis and prediction tasks. The AI-powered chatbot receives support from natural language processing (NLP) through Google Dialog flow to offer immediate relaxation techniques and music suggestions and yoga exercises to users. The platform allows users to track their mental health progress through an interactive control centre that provides AI-generated personalized reports and downloadable assessments.
The system activates automated alert systems together with individual recommendations to simultaneously detect mental health warning indicators while implementing prompt assistance for better well-being. The platform combines AI analytics with chatbot equivalent and interactive tracking capabilities to establish itself as a groundbreaking instrument which boosts mental health education while providing active time-based help for those needing assistance.
Keywords: AI-driven mental health diagnosis, machine learning algorithms, mental well-being, behavioural patterns, exploratory data analysis (EDA), predictive modelling, natural language processing (NLP), real-time support, personalized recommendations, interactive dashboard, mental health tracking, early intervention, stress management, workplace well-being.
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DOI:
10.17148/IJARCCE.2025.14313
