A Stacking Model-Based Classification Algorithm Is Used to Predict Social Phobia

Abstract

University students, as a special group, face multiple psychological pressures and challenges, making them susceptible to social anxiety disorder. However, there are currently no articles using machine learning algorithms to identify predictors of social anxiety disorder in university students. This study aims to use a stacked ensemble model to predict social anxiety disorder in university students and compare it with other machine learning models to demonstrate the effectiveness of the proposed model. AUC and F1 are used as classification evaluation metrics. The experimental results show that in this dataset, the model combining logistic regression, Naive Bayes, and KNN algorithms as the first layer and Naive Bayes as the second layer performs better than traditional machine learning algorithms. This provides a new approach to studying social anxiety disorder.

Keywords

• college students
• social phobia
• machine learning classification algorithm
• stack model