Mental Health Diagnosis Survey Using Question Reduction, Advanced Generation Algorithms and Contactless UI System

In this fast-moving world, prioritizing mental health has become very important for overall wellbeing, this paper present a software-based survey to assess and diagnose mental health symptoms effectively. The system has user friendly VI and tries to maximize the user engagement through various machine learning models. The system effectively addresses various issues of data storage in present systems ensuring that the critical mental health cases are addressed properly. In addition to this it also incorporates a question reduction algorithm which reduces the time taken to give the survey, which enhances user engagement and thereby enhances validity and user completion rates of survey. Considering future needs, the system integrates natural language processing and web scraping to dynamically generate questions related to current news affairs through which future mental health symptoms can be incorporated into the survey. Such systems ensure that the models stay relevant and responsive to upcoming mental health challenges.