چهارشنبه 24 مرداد 1403
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دوره 12، شماره 1 - ( زمستان 1402 )
جلد 12 شماره 1 صفحات 12-1 |
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Nooripour R, Ilanloo H, Naveshki M, Naveshki S, Amiri Majd M, Sikström S et al . Evaluation of COVID-19 Stress in University Students According to Their Socio-demographic Characteristics Based on Machine Learning Algorithms. PCP 2024; 12 (1) :1-12
URL: http://jpcp.uswr.ac.ir/article-1-895-fa.html
URL: http://jpcp.uswr.ac.ir/article-1-895-fa.html
Evaluation of COVID-19 Stress in University Students According to Their Socio-demographic Characteristics Based on Machine Learning Algorithms. Practice in Clinical Psychology. 1402; 12 (1) :1-12
چکیده: (1479 مشاهده)
Objective: The coronavirus pandemic has presented a significant challenge and brought about dramatic changes for universities and their students. This study evaluated machine learning algorithms for estimating COVID-19 stress levels among Iranian university students.
Methods: We conducted an online survey from May 10th to November 20th, 2021, to determine how Iranian university students responded to the COVID-19 outbreak in Iran. The survey invitations were sent to Iranian university students via e-mail, forums, and social media platforms, such as internet advertisements. We collected data from 3490 university students, using sociodemographic characteristics and the COVID-19 Stress Scale (CSS; Nooripour et al. [2022]). The adaptive neuro-fuzzy inference system (ANFIS) network for prediction and fuzzy logic-based rules were used for analyzing the data. For classification, eight machine learning algorithms were employed: support vector machine (SVM), K-nearest neighbors (KNN), random forest, multilayer perceptron, decision tree, and passive-aggressive algorithm. These algorithms were selected based on their principles and suitability for stress detection in the desired category.
Results: Among the algorithms, the decision tree algorithm showed the best performance in accurately classifying the data into the correct stress intensity categories. Moreover, analyses revealed that gender, age group, and education significantly influenced stress intensity levels, with men experiencing less stress; stress intensity decreased with age, and higher education was associated with lower stress levels. The results indicated that education and marital status were the most influential parameters for all three top-performing algorithms (random forest, multi-layer perceptron, and decision tree).
Conclusion: Our research suggests that innovative methods such as machine learning algorithms can be used to evaluate psychological distress caused by the COVID-19 outbreak, such as stress. Evaluating stress levels can help prevent mental health problems and enhance students’ coping capabilities.
Methods: We conducted an online survey from May 10th to November 20th, 2021, to determine how Iranian university students responded to the COVID-19 outbreak in Iran. The survey invitations were sent to Iranian university students via e-mail, forums, and social media platforms, such as internet advertisements. We collected data from 3490 university students, using sociodemographic characteristics and the COVID-19 Stress Scale (CSS; Nooripour et al. [2022]). The adaptive neuro-fuzzy inference system (ANFIS) network for prediction and fuzzy logic-based rules were used for analyzing the data. For classification, eight machine learning algorithms were employed: support vector machine (SVM), K-nearest neighbors (KNN), random forest, multilayer perceptron, decision tree, and passive-aggressive algorithm. These algorithms were selected based on their principles and suitability for stress detection in the desired category.
Results: Among the algorithms, the decision tree algorithm showed the best performance in accurately classifying the data into the correct stress intensity categories. Moreover, analyses revealed that gender, age group, and education significantly influenced stress intensity levels, with men experiencing less stress; stress intensity decreased with age, and higher education was associated with lower stress levels. The results indicated that education and marital status were the most influential parameters for all three top-performing algorithms (random forest, multi-layer perceptron, and decision tree).
Conclusion: Our research suggests that innovative methods such as machine learning algorithms can be used to evaluate psychological distress caused by the COVID-19 outbreak, such as stress. Evaluating stress levels can help prevent mental health problems and enhance students’ coping capabilities.
نوع مطالعه: كاربردي |
موضوع مقاله:
روانپزشكي
دریافت: 1402/3/12 | پذیرش: 1402/5/22 | انتشار: 1402/10/11
دریافت: 1402/3/12 | پذیرش: 1402/5/22 | انتشار: 1402/10/11
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