Hues of Mental Health: Harnessing Cat Swarm Optimization and Deep Learning for Anxiety and Depression Detection

Abstract

Systems that provide health care face major problems because anxiety and depression disorders affect people more now across the entire world. Researchers find that traditional testing approaches based on patient interviews tend to produce doubtful results. This study looks at how combining Cat Swarm Optimization and Deep Learning algorithms can help doctors find anxiety and depression sooner at the start of the problem. The CSO optimization technique finds the best features of data and deep neural networks help study and process complex datasets. The researchers develop a better mental health diagnosis system using both Deep Learning and Cat Swarm Optimization. The integrated system produces better results than traditional methods and detects mental health conditions at higher speed.