Abstract
Objectives. The purpose of the study was to substantiate the influence of physical activity on stress-associated conditions in higher education students.
Materials and methods. The dataset for building the models consisted of 1115 observations, 16 independent and 3 dependent variables. As the main method we used the random forest method, the idea of which is to obtain a forecast by aggregating the predictions of a set of individual decision trees, each of which is trained on a data subset isolated from the studied sample.
Results. Physical activity (PA) was found to be the most important factor in predicting stress-related conditions in university students. In addition, PA levels involving moderate and high levels of energy expenditure, as well as the number of stressful events experienced, played a significant role in predicting stress among students. In order to predict stress-related conditions in higher education students, the models “Stress”, “Increased anxiety”, and “Risk of PTSD” were built using the random forest method. The model “Stress” had the highest quality: its Accuracy was 0.77, Recall – 0.86, Precision – 0.79, and F1 Score – 0.82. The “PTSD Risk” model correctly predicted 78% of cases that indicates its good overall performance, however it correctly identified only 23% of the students who actually had the signs of this disorder. Regarding the state of anxiety, given that it is less stable than stress and PTSD, which can make model training difficult, the model built had an average accuracy of 56%, as well as reduced completeness and balance.
Conclusions. Models for predicting increased anxiety and identifying students with signs of PTSD require further improvement. The implementation of developed models allows to quickly identify the manifestations of stress-related conditions in higher education students and to take the necessary measures based on the engagement in PA to prevent the development of stress-related disorders.