Explainable machine learning models to understand determinants of COVID-19 mortality in the United States

Abstract

COVID-19 is now one of the leading causes of mortality amongst adults in the United States for the year 2020. Multiple epidemiological models have been built, often based on limited data, to understand the spread and impact of the pandemic. However, many geographic and local factors may have played an important role in higher morbidity and mortality in certain populations. The goal of this study was to develop machine learning models to understand the relative association of socioeconomic, demographic, travel, and health care characteristics of different states across the United States and COVID-19 mortality. Using multiple public data sets, 24 variables linked to COVID-19 disease were chosen to build the models. Two independent machine learning models using CatBoost regression and random forest were developed. SHAP feature importance and a Boruta algorithm were used to elucidate the relative importance of features on COVID-19 mortality in the United States. Feature importances from both the categorical models, i.e., CatBoost and random forest consistently showed that a high population density, number of nursing homes, number of nursing home beds and foreign travel were strongest predictors of COVID-19 mortality. Percentage of African American amongst the population was also found to be of high importance in prediction of COVID-19 mortality whereas racial majority (primarily, Caucasian) was not. Both models fitted the data well with a training R2 of 0.99 and 0.88 respectively. The effect of median age,median income, climate and disease mitigation measures on COVID-19 related mortality remained unclear. COVID-19 policy making will need to take population density, pre-existing medical care and state travel policies into account. Our models identified and quantified the relative importance of each of these for mortality predictions using machine learning.

Applications of artificial intelligence in battling against covid-19: A literature review

“…To study this, various machine learning models are proposed in [432] to extract the relationship between the spread of the disease and factors like weather variables, temperature and humidity. In [433] , 24 variables linked to covid-19 are used to build a model with CatBoost regression and random forest algorithms. The work uses SHAP feature importance and Boruta algorithm to find the relative importance of features on covid-19 mortality in the US.…”

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID‐19 from Chest Radiographical Images: A Survey

“…Hasan et al [90] utilized Q-Q plot and ARIMA model to present the death rate based on time-series data of different states of India. Many other researchers [91][92][93][94][95][96][97][98][99] have presented the applications of Machine Learning approaches for early diagnosis and predicting the trends of COVID-19. Wang et al [100] presented the epidemic trend using logistic and time-series prediction model called FbProphet.…”