Predictive Machine Learning Models for Assessing Lebanese University Students' Depression, Anxiety, and Stress During COVID-19

• Main Authors: Christo El Morr (Author), Manar Jammal (Author), Imad Bou-Hamad (Author), Sahar Hijazi (Author), Dinah Ayna (Author), Maya Romani (Author), Reem Hoteit (Author)
• Format: Book
• Published: SAGE Publishing, 2024-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

University students are experiencing a mental health crisis. COVID-19 has exacerbated this situation. We have surveyed students in 2 universities in Lebanon to gauge their mental health challenges. We have constructed a machine learning (ML) approach to predict symptoms of depression, anxiety, and stress based on demographics and self-rated health measures. Our approach involved developing 8 ML predictive models, including Logistic Regression (LR), multi-layer perceptron (MLP) neural network, support vector machine (SVM), random forest (RF) and XGBoost, AdaBoost, Naïve Bayes (NB), and K-Nearest neighbors (KNN). Following their construction, we compared their respective performances. Our evaluation shows that RF (AUC = 78.27%), NB (AUC = 76.37%), and AdaBoost (AUC = 72.96%) have provided the highest-performing AUC scores for depression, anxiety, and stress, respectively. Self-rated health is found to be the top feature in predicting depression, while age was the top feature in predicting anxiety and stress, followed by self-rated health. Future work will focus on using data augmentation approaches and extending to multi-class anxiety predictions.

Item Description

2150-1327
10.1177/21501319241235588