Comparative Machine Learning Approaches for Classifying Student Stress Types

Abstract

Student stress is a major mental health concern that may affect academic performance, psychological well-being, and quality of life. Early identification of stress types is important because different stress categories may require different intervention strategies. With advances in data-driven technologies, machine learning has emerged as a promising approach for identifying patterns in mental health data and supporting automated assessment. This study aims to classify student stress types using the Stress Indicators Dataset for Mental Health Classification, which contains 2,000 student responses. The dataset includes demographic information, psychological symptoms, academic challenges, physical health indicators, and social factors associated with stress. Before model development, preprocessing procedures included data cleaning, duplicate removal, filtering extreme age values, and exploratory data analysis (EDA). Feature engineering was also performed by creating three composite variables, namely symptom_score, academic_stress_score, and social_stress_score, to represent broader stress-related dimensions. Three classification algorithms, Logistic Regression, K-Nearest Neighbors (KNN), and Gaussian Naive Bayes, were developed and evaluated using confusion matrix analysis and Accuracy, Precision, Recall, and F1-Score metrics. The dataset was split into training and testing subsets using an 80:20 stratified split to preserve class distribution. Results indicate that Logistic Regression achieved the highest performance, with an accuracy of 98.10%, precision of 98.14%, recall of 98.10%, and F1-score of 97.96%. Feature engineering slightly improved KNN performance but did not enhance Logistic Regression or Gaussian Naive Bayes. These findings demonstrate the potential of machine learning as a supporting tool for student stress assessment and monitoring, enabling identification of stress categories.