Analyzing Impact of Socio-Economic Factors on COVID-19 Mortality Prediction Using SHAP Value

TL;DR

This study uses machine learning and SHAP values to interpret how socio-economic factors influence COVID-19 mortality predictions, providing insights into feature impacts at both global and individual levels.

Contribution

It introduces a method to interpret socio-economic factors affecting COVID-19 mortality predictions using SHAP values, focusing on understanding rather than just prediction accuracy.

Findings

Household income, age, education, and employment significantly influence predictions.

SHAP analysis reveals both global and local feature impacts.

Insights into individual patient data impact on mortality prediction.

Abstract

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.