Predicting Susceptibility to Covid Stress Using Data Mining

Coronavirus (COVID-19) had major impacts on the daily lives of people. Lock-downs, work from home situations, loss in jobs, market changes, and less communication, and interaction between people especially during the stressful Covid period have made them more vulnerable to mental health issues, depression, loneliness, etc. With Covid related healthcare being given priority, the mental health issues faced by the public that has been both directly and indirectly affected by it have been majorly left ignored. These issues need to be taken care of by people on individual level and by the government for better public health. Hence, in this paper we introduce the emerging technique of data mining into the Covid-19 linked mental health for predicting the susceptibility of the general public around the globe to mental health side effects as a result of covid and pandemic circumstances. We used the COVIDiSTRESS survey data containing 103825 instances of people across the globe to identify the people more susceptible to Covid related stress. Logistic regression, random forest, xgboost, AdaBoost, and gradient boosting classifier were applied to the processed data giving an accuracy of 88.12%, 88.89%, 88.73%, 88.60%, and 89.25% respectively. The Models predicted the people who are likely to face covid stress based on different independent factors like their demographic variables, trust of authorities, corona concerns etc. The stress factor was measured using PSS-10 variable included in the survey. The result showed that the model developed with Gradient Boosting Classifier is found to be the most efficient model with an accuracy of 89.25%. Our analysis also showed that females, divorced/widowed people and full-time employees were more prone to stress amongst others in the gender/marital status/employment category.