AWNB: Augmented-Wingsuit–Optimized Multinomial Naïve Bayes for Multimodal Early-Warning of College Student Mental Health

Abstract

Early identification of mental health risks among college students is critical for timely intervention, promoting well-being, and supporting academic performance. This study utilizes a comprehensive multimodal dataset comprising 1,000 students, integrating behavioral routines (study hours, sleep schedules, and daily activity patterns), physiological indicators (heart rate, stress levels, and sleep quality), and social engagement measures (messaging frequency and participation in clubs or events) to classify students into Low, Moderate, and High mental health risk categories. Data preprocessing included handling missing values with mean/median imputation for continuous features and mode imputation for categorical features, followed by standardization using Z-score normalization. Stratified five-fold cross-validation with a fixed random seed was applied to ensure reproducible and unbiased evaluation. Baseline models, including the Data Fusion Model, CASTLE, YOLOv8, Time-Aware Multimodal Fusion Network (TAMFN), and Random Forest combined with CatBoost, were carefully tuned under equivalent computational budgets to provide fair comparisons. The proposed Augmented Wingsuit–Enhanced Multinomial Naïve Bayes (AWNB) framework combines optimization-driven hyperparameter tuning with decision-level multimodal fusion, effectively capturing complex interactions between behavioral, physiological, and social features. Experimental results demonstrate that AWNB achieves superior performance, with 97.41% accuracy, 95.14% precision, 93.67% recall, and 94.82% F1-score. Baseline performances were: Data Fusion Model – 95.2% accuracy, 93.7% precision, 90.8% recall, 92.2% F1-score; CASTLE – 84.47% accuracy, 71.47% recall, 74.65% F1-score; YOLOv8 – 71% precision, 74.1% recall; TAMFN – 66.02% precision, 66.50% recall, 65.82% F1-score; and Random Forest + CatBoost – 91.3% accuracy, 92.4% precision, 90.5% recall. All metrics are reported as mean ± standard deviation, and statistical significance was validated using paired tests. These findings establish AWNB as a robust, interpretable, and computationally efficient framework, outperforming existing approaches while enabling scalable application in academic mental health monitoring.