Background: Artificial intelligence and machine learning have enormous potential to deal efficiently with a wide range of issues that traditional sciences may be unable to address. Neuroscience, particularly psychiatry, is one of the domains that could potentially ben- efit from artificial intelligence and machine learning. This study aims to predict Stress and assess Coping with stress mechanisms during the COVID-19 pandemic and, therefore, help establish a successful intervention to manage distress.
Methods: COVIDiSTRESS global survey data was used in this study and comprised 70 652 respondents after pre-processing. Binary classification is performed for predicting Stress and Coping with stress, while 2 ensemble machine learning algorithms, deep super learner and cascade deep forest, and state-of-the-art methods are explored for classifica- tion. Correlation attribute evaluation is used for feature significance. Statistical analysis, such as Cronbach’s alpha, demographic statistics, Pearson’s correlation coefficient, inde- pendent sample t-test, and 95% CI, is also performed.
Results: Globally, females, the younger population, and those in COVID-19 risk groups are observed to possess higher levels of stress. Trust, Loneliness, and Distress are found to be the primary predictors of Stress, whereas the significant predictors for coping with stress are identified as Social Provision, Extroversion, and Agreeableness. Deep super learner and cascade deep forest outperformed the state-of-the-art methods with an accuracy of up to 88.42%.
Conclusions: By comparing different classifiers, we can conclude that multi-layer ensem- ble outperforms all. Another aim of this study, is the ability to regulate demographic and negative psychological states with a goal of medical interventions and to work towards building multiple coping strategies to reduce stress and promote resilience and recovery from COVID-19.
Cite this article as: Tigga NP, Garg S. Prediction of global psychological stress and coping induced by the COVID-19 outbreak: A machine learning study. Alpha Psychiatry. 2022;23(4):193-202.