Association Rule Algorithm-Based Prediction Model for SCL-90 Psychological Measurement: A Comparative Analysis Before and During the COVID-19 Pandemic

Qiang Wang; Daisy S. Yap

doi:10.54097/cxq6e7q5

Authors

Qiang Wang
Daisy S. Yap

DOI:

https://doi.org/10.54097/cxq6e7q5

Keywords:

Psychological Measurement, SCL90, COVID-19, Machine Learning, Apriori, Association Rule

Abstract

A prediction model for SCL-90 psychometric data was developed by association rule algorithm. The SCL-90 data reported by college students were used to predict possible psychological symptoms and psychological states by mining the association rules in the data. After the experiment, it is found that there are significant differences in the SCL-90 data in different time periods. As an example, the outbreak of COVID-19 had a significant impact on the mental health of college students in 2019 and 2020.The data in 2019 showed relatively few fixated mental states, while the data in 2020 showed a significant increase in fixated mental states. It can be inferred that this difference is related to the negative impact of the COVID-19 outbreak on mental health, with the COVID-19 outbreak increasing anxiety, which in turn led to the emergence of psychological symp-toms. Based on these findings, it is important to focus not only on physical health but also on changes in mental health in future mental health interventions and prevention efforts. The predictive model derived from this study can inform mental health management and help identify possible psychological symptoms and psychological states and take appropriate inter-ventions.

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