Deep Learning Approaches to Assessing University Students' Health-Related Quality of Life: A Comparative Study of MLP and GNN

Health-related quality of life (HRQoL) is a multidimensional construct reflecting individuals' overall well-being, including physical, mental, emotional, and social aspects. This study focuses on assessing HRQoL among university students. A comprehensive survey, incorporating several validated instruments was administered to a stratified sample of undergraduate and graduate students. To analyze the collected data, we developed two deep learning models: a Fully Connected Neural Network (FCNN) and a Graph Neural Network (GNN). The GNN model represents each student's responses as a tree-like graph and processes this structured data through multiple convolutional layers enhanced by a sort-pooling layer, which standardizes graphs of varying dimensions. Experimental results indicate that both models effectively classify students based on their HRQoL profiles. Notably, the GNN model achieves higher test accuracy and F-Score compared to the FCNN, demonstrating robust performance even when analyzing incomplete surveys. These findings underscore the potential of graph-based deep learning methods by integrating heterogeneous data and capturing complex relationships, these approaches offer a promising solution for monitoring and improving students' well-being. Index Terms-Deep Learning, Graph Neural Networks, HRQoL