Abstract:
Psychological distress among university students in Sri Lanka is an increasing issue, which is
compounded by academic, financial, and lifestyle issues. Traditional screening tools do not
effectively predict the "At-risk" students early enough to help them be treated in time. This
study will create interpretable machine learning model early binary classification of psycho-
logical risk among Sri Lankan university students using behavioral, demographic, and clinical
indicators. This ML model will be able to identify whether a student is “At-risk” or “Not-at-
risk” based on the behavioral, demographic, and clinically validated indicators. The survey
data were obtained on 500 undergraduate students who represented various faculties and con-
tained demographic factors, lifestyle behaviors, and results of the Depression, Anxiety and
Stress Scale (DASS-21). The responses on DASS21 were converted to a binary risk label us-
ing clinical scoring guidelines, in which a moderate or greater severity on any of the subscales
was a precursor of psychological risk. Several baseline models such as Logistic Regression,
Support Vector Machine, Decision Tree and Gradient Boosting were trained and assessed on
the basis of accuracy, precision, recall, F1-score, and ROC AUC. Logistic Regression exhibited
the highest precision (90%) which is important in order to reduce false positives at the cost of
screening mental health, as the regression showed the highest accuracy (84%) and AUC-ROC
(90.8%). The critical predictors of psychological risk were severity of DASS-21, sleep distur-
bances, spending much time on the screen, and lack of social contact. Interpretable machine
learning models especially the Logistic Regression are a feasible and scalable method of early
psychological risk detection in universities. This instrument enables proactive, data-driven sup-
port for university counseling services in Sri Lanka.
