Concise multi-class anxiety disorder risk assessment: A novel advanced machine learning approach

Abstract

Rapidly assessing anxiety disorder risk is crucial for effective mental health screen and intervention. However, traditional survey tools such as DASS-42 are time-consuming in responding and scoring. We used a novel advanced machine learning approach to create a concise anxiety disorder scale based on DASS-42. By applying advanced ML techniques and feature selection, we created a concise version of the anxiety risk scale while maintaining high validity. The resulting model requires fewer questions to predict anxiety risk levels effectively. This optimized scale was implemented in an online tool for quick self-screening and clinical use. This innovation holds significant societal implications, offering scalable, efficient, and accurate methods that facilitate faster and earlier anxiety disorder detection and intervention, especially among underserved and high-risk populations. The study highlights how machine learning can create practical, accessible mental health assessment tools, contributing to improved well-being outcomes.