A hybrid approach to evaluating mental health indicators and predicting student mental health risk
Emine Rumeysa AtmacaPurpose
This study examines relational closeness between university students' academic achievement, represented by cumulative grade point average (CGPA) and mental health, well-being, sociodemographic variables using a self-reported student survey. Anxiety, depression, stress, sleep quality, diet quality, physical energy level, age, gender, department and scholarship status were analyzed. Relational and predictive relevance was evaluated without assuming causality, with an ergonomic interpretation of well-being indicators.
Design/methodology/approach
Grey relational analysis (GRA) estimated relational degrees between CGPA and selected variables. Deng's, absolute, relative, comprehensive and negative GRA models were applied, with sensitivity analyses across distinguishing coefficient values. Cronbach's alpha and exploratory factor analysis (EFA) examined whether anxiety, depression and stress represented a common structure. They were combined into a risk construct defined as “mental health risk.” Seven machine learning algorithms classified this construct. Repeated randomized train-test experiments assessed accuracy, precision, F1-score and receiver operating characteristic (ROC) area under the curve (AUC).
Findings
Anxiety, depression and stress showed relatively high relational degrees with CGPA compared with the other variables. These findings suggest that academic achievement is related not only to demographic characteristics but also to psychological well-being and ergonomics-related conditions of students. According to the machine learning results, AdaBoost achieved the highest mental health risk classification performance, with an accuracy of 82.98%.
Originality/value
This study integrates comparative GRA modeling, statistical construction of a mental health risk indicator, repeated machine learning evaluation and ergonomic interpretation. The framework links relational analysis with predictive classification while distinguishing analytical risk from clinical diagnosis.