Perceived Stress Prediction among Working Employees Based on CAT-DCNN Technique

Nowadays, stress diseases affect a large portion of the IT workforce. Workers are more likely to experience stress as a result of evolving work cultures and ways of living. Workplace mental health is still a major issue, despite many efforts by businesses and other organizations to address the issue. The primary objective of it is to determine the causes of the high rates of stress experienced by working-age adults by analyzing their stress patterns. Preprocessing, feature selection, and training the model all rely on sequence. When getting data ready to use in data mining methods, a series of procedures known as “data preprocessing for data mining” is employed. Spearman correlation analysis is employed in feature selection. When training a CAT-DCNN model, feature selection is paramount. When compared to two cutting-edge algorithms, CAT and DCNN, the proposed method comes out on top. With a precision of 97.41%, the results showed a remarkable improvement.