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An Alternative Approach for Detecting Problematic Alcohol Use: Developing the Student Alcohol Risk Assessment
Şükrü Alperen Korkmaz1,2, Pınar Mutlu1,3, Sibel Oymak1,4
1Çanakkale Onsekiz Mart University Center for Combating Addiction Application and Research, Çanakkale, Türkiye.
Background/Aim:
Risky alcohol use is common among university students and negatively impacts physical and psychosocial health. Current screening instruments focus only on behavioral indicators, neglecting psychosocial factors. This study aimed to develop an AI-supported, brief student-focused tool for assessing alcohol-related risk and to evaluate its preliminary reliability and validity among university students.
Materials And Methods:
A total of 599 university students participated in this cross-sectional study. From a pool of 59 items covering behavioral, psychological, social, and academic questions related to alcohol use, a new risk model was created using 15 items selected with ChatGPT-supported AI algorithms (SARAS-15). Risk scores ranged from 0 to 27, categorizing participants into low, medium, and high-risk groups. The model's performance was evaluated using machine learning methods such as Random Forest, Logistic Regression, SVM, and KNN, along with cross-validation and ROC analysis. We also analyzed its internal consistency and its correlations with screening instruments (AUDIT, RAPS4-QF, CAGE).
Results:
In machine learning analyses, the logistic regression model achieved the highest performance (93.5% accuracy, F1-score = 0.864, sensitivity = 0.826, specificity = 0.959). ROC analysis demonstrated excellent discrimination between low-risk (AUC = 0.96) and high-risk (AUC = 0.93) groups, with strong discrimination for the intermediate-risk group (AUC = 0.88). The Random Forest model achieved an overall accuracy of 87%, successfully differentiating between the low-risk group (F1 score = 0.91) and the high-risk group (F1 score = 1.00). The new model's Cronbach's alpha was 0.811, with strong convergent validity correlations with screening instruments (AUDIT, r = 0.861; RAPS4-QF, r = 0.793; CAGE, r = 0.631).
Conclusion:
The developed artificial intelligence-supported 15-item new risk score model is valid and reliable in assessing risky alcohol use among university students. This scale demonstrates a high level of agreement with traditional tests and can accurately detect three risk levels. Due to its multi-domain content structure, which addresses both behavioral and psychosocial effects, it serves as a complementary screening tool for early risk identification and intervention planning.