Regression for Predicting COVID-19 Infection Possibility Based on Underlying Cardiovascular Disease: A Medical Score-Based Approach

Abstract

The appearance of a novel coronavirus (COVID-19) has presented an immense challenge for the healthcare community around the world. Many patients with COVID-19 have primary cardiovascular (CV) sickness or create intense heart injury throughout the infection. These patients are at exceptionally great danger from COVID-19 because of their fragility and powerlessness for a myocardial involvement. Good comprehension of the exchange between COVID-19 and CV illness is required for these patients’ ideal administration. As a growing range of applications for patient management and system incorporation in real time is available, artificial intelligence (AI) can play a decisive role in the emergency department (ED), in fields such as intelligent monitoring, the estimation of clinical results, and resource planning. The proposed system aims to develop an adaptation of a smart medical evaluation method to decide if people with an underlying cardiovascular health disorder would contract COVID-19 based on the limited range of pre-selected variables deemed scientifically necessary and easily calculated when designing clinical judgment regulations.