Analysis of Ventilator Allocation to Coronavirus Patients with Demographic Data and Machine Learning Methods

Abstract

The allocation of limited resources and the fair treatment to patients are among the important bioethical issues addressed in the treatment of coronavirus. This study analyzes the relationships between COVID- 19 recovery rate and ventilator allocation time and demographic variables such as gender, race, age, and insurance status. In this study, the data on coronavirus patients of a hospital in the USA were used. Machine learning methods including Random Forest, Decision Tree, Support Vector Machine and Logistic Regression algorithms were utilized to model the data. As a result, that patients covered by Medicaid insurance, which benefits the poor people in the US, are less likely to survive as a result of coronavirus. This result is important in terms of discussing the relationship between health insurance and bioethics.

Keywords

• covid-19
• coronavirus
• ventilator
• triage
• demographics
• health insurance
• medicaid
• machine learning
• random forest
• decision tree
• support vector machine
• logistic regression