Evaluating the Performance of Selected Single Classifiers with Incorporated Explainable Artificial Intelligence (XAI) in the Prediction of Mental Health Distress Among University Students

Abstract

The mental health of university students has emerged as a critical global public health concern, with increasing prevalence of anxiety, depression, and stress-related conditions reported across diverse academic environments. With the increasing mental health issues among university students all over the world, there is an observable disparity in employing interpretable machine learning models to assess the risks of psychological distress, especially in resource-limited countries such as Kenya. This study bridges this gap by evaluating the predictive performance of selected single machine learning classifiers; Multinomial Logistic Regression (MLR), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) with Explainable Artificial Intelligences (XAI) in identifying levels of mental health distress (Low, Moderate, and High) among university students in Tharaka Nithi County, Kenya. A structured questionnaire was administered to a stratified random sample of 1500 students, capturing comprehensive data across demographic, academic, psychosocial, and behavioural dimensions. Data were preprocessed, encoded, and partitioned into 70% training and 30% testing sets. Models were developed using 10-fold cross-validation, with hyperparameter tuning performed via grid search. Explainable Artificial Intelligence (XAI) techniques, including SHAP (Shapley Additive Explanations) and model breakdown plots, were integrated to enhance transparency and interpretability of the model. The Support Vector Machine model demonstrated superior performance, with an overall accuracy of 97.6%, a Kappa coefficient of 0.957, and a perfect Area Under the Curve (AUC) score of 1.000 across all levels of mental distress. The model achieved a sensitivity of 1.000 for both High and Low distress, and 0.960 for Moderate, with precision values of 0.880, 0.960, and 1.000, respectively. KNN followed with an accuracy of 73.9% and Kappa of 0.471, while MLR and NB achieved accuracies of 69.7% and 68.8%, respectively. The SVM model emerged as the best model due to its ability to handle nonlinear and complex patterns in the data. SHAP analysis identified "Lifestyle and Health Factors," "Personal and Mental Health," "Academic Pressure," and "Quarter Life Crisis" as the most influential predictors across models. The study concludes that interpretable machine learning approaches, particularly SVM augmented with XAI, can provide highly accurate and actionable insights into student mental health. The study recommends integrating such models into institutional mental health surveillance frameworks to support early detection, personalized interventions, and policy planning, aligning with Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being for all.

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