  This paper applies multiple machine learning (ML) algorithms to a dataset of
de-identified COVID-19 patients provided by the COVID-19 Research Database. The
dataset consists of 20,878 COVID-positive patients, among which 9,177 patients
died in the year 2020. This paper aims to understand and interpret the
association of socio-economic characteristics of patients with their mortality
instead of maximizing prediction accuracy. According to our analysis, a
patients households annual and disposable income, age, education, and
employment status significantly impacts a machine learning models prediction.
We also observe several individual patient data, which gives us insight into
how the feature values impact the prediction for that data point. This paper
analyzes the global and local interpretation of machine learning models on
socio-economic data of COVID patients.
