Planning for a Crisis: Predicting Anxiety in a Population During COVID-19 Using Machine Learning.

Abstract

COVID-19 impact on population mental health has been reported around the world. Statistics Canada has conducted a survey among Canadian population to gauge mental health challenges they experienced, specifically in terms of anxiety. We create a machine learning model to predict anxiety symptoms as measured by the General Anxiety Scale among the sample of 45,989 respondents to the survey. Eight algorithms including Logistic Regression, Random Forest, Naive Bayes, K Nearest Neighbours, Adaptive boost, Multi linear perceptron, XGBoost and LightBoost. LightBoost provided the highest performing model AUC score (AUC=87.45%). In addition, the features "perception of mental health compared to before physical distancing", "perceived life stress", and "perceived mental health" were found to be the most important three features to predict anxiety. A limitation of this study is that the sample is not representative of the Canadian population. Preparing for virtual care interventions during a crisis need to take into considerations these factors.