Farhad Lotfi‡, Branka Rodić§, Aleksandra Labus‡, Zorica Bogdanović‡Corresponding author: Farhad Lotfi ( farhadlotfi1990@gmail.com ) © Farhad Lotfi, Branka Rodić, Aleksandra Labus, Zorica Bogdanović. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. Citation:
Lotfi F, Rodić B, Labus A, Bogdanović Z (2024) Smart healthcare: developing a pattern to predict the stress and anxiety among university students using machine learning technology. JUCS - Journal of Universal Computer Science 30(10): 1316-1341. https://doi.org/10.3897/jucs.116174 |  |
Background: Anxiety among students has become a fairly major problem. In the current era, Machine Learning (ML) can be used as a quick technology to predict students' anxiety with the high-level accuracy.
Objectives: This research aims to predict university students' anxiety by using supervised learning algorithms with providing pertinent feedback.
Methods: A total of 231 students from the University of Belgrade filled out the standard questionnaire called the State-Trait Anxiety Inventory (STAI). In addition, deeper information related to students’ anxiety like physical activity, Grade Point Average (GPA), and smoking cigarettes were collected. The Linear Regression algorithm was chosen to examine STAI using Python.
Results: Linear regression as an appropriate algorithm was exploited for this purpose. The accuracy metric obtained by using the Mean Absolute Error (MAE), was 7.86 for state anxiety and 5.68 for trait anxiety. In addition, the Mean Squared Error (MSE) has also been calculated with state anxiety at 7.80, and trait anxiety at 9.66. Moreover, to find the factor with the highest impact after training, a regression analysis method (LASSO) was used. K-Nearest Neighbour (KNN) algorithm also checked the accuracy of training by overfitting and underfitting.
Conclusion: The purpose of this study was the analysis of anxiety factors with the highest impact as well as the analysis of the STAI by linear regression to improve a smart healthcare model by discovering an acceptable output with the highest accuracy.