Experimental Analysis of Employee Depression Identification System Using Modified Learning Procedure with AI Assistance

This study presents an innovative approach for employee depression identification by integrating advanced Artificial Intelligence (AI) techniques. The Modified Learning Procedure with AI Assistance (MLPAI) is employed, combining Feature Extraction through RF, Classification using AlexNet, and Optimization facilitated by the Mayfly Optimization Algorithm The res earch aims to improve the precision of depression identification within the workforce. The Feature Extraction phase employs RF to robustly extract relevant features from diverse datasets. AlexNet, a deep learning classification model, is then employed for accurate categorization. The Mayfly optimization algorithm optimizes the system and fine-tuning parameters for increased precision. The experimental analysis involves a targeted examination of Millennial and Generation Z employees in South Korea, aged between 20 and 40. The implemented MLPAI system achieved an impressive accuracy of 97.34%, demonstrating its efficacy in identifying employee depression. The entire system has been implemented and documented in a Jupyter Notebook, providing a transparent and accessible framework for researchers and practitioners interested in replicating or extending this study. Through this comprehensive approach, the study aims to contribute valuable insights into the intersection of AI, mental health, and workplace dynamics, offering a novel framework for addressing and mitigating employee depression.