Application of Categorical Naive Bayes for Classifying Student Stress Levels Based on Academic and Lifestyle Factors

Abstract

Student stress has become an important issue in higher education due to increasing academic demands and lifestyle pressures. Early identification of student stress levels is necessary to support appropriate interventions. This study applies a machine learning approach to classify student stress levels based on academic and lifestyle factors using the Categorical Naive Bayes algorithm. The dataset was obtained from a student stress survey consisting of categorical attributes and stress level labels ranging from 1 to 5. Data preprocessing was conducted to ensure compatibility with the classification model, followed by data splitting into training and testing sets using an 80:20 ratio. Model performance was evaluated using a confusion matrix and standard classification metrics, including accuracy, precision, recall, and F1-score. The experimental results show that the proposed model achieved an accuracy of 0.50, with weighted average values of 0.52 for precision, 0.50 for recall, and 0.50 for F1-score. These results indicate that Categorical Naive Bayes is capable of performing stress level classification with moderate performance on categorical survey data. This study demonstrates the potential of machine learning techniques as a supporting tool for analyzing student stress levels and provides a basis for further model improvement in future research.