Predicting the stress level of students using Supervised Machine Learning and Artificial Neural Network (ANN)

Nowadays, the concept of stress is universally acknowledged. Many of us face situations that contribute to daily hassles, affecting professionals such as teachers, doctors, lawyers, journalists, and parents. University students are also encountering similar challenges. This study aims to identify the factors generating stress among students at Tribhuvan University Dharan in Nepal. We can predict and prevent stress at its early stages by analyzing these stress factors. This paper proposes various machine learning and deep learning models, including support vector machine (SVM), Random Forest, Gradient Boosting, AdaBoost, CatBoost, LightGBM, ExtraTree, XGBoost, logistic regression, K-nearest neighbor (KNN), Naive Bayes, decision tree, multi-layer perceptron (MLP), and artificial neural network (ANN). The Naive Bayes model achieved an accuracy of 90%, while SVM had the lowest test accuracy at 85.45%. The accuracy of these models improved with hyperparameter tuning. The key finding of this study is that the "academic period" is the most stressful time for students compared to other situations.