Machine Learning Approaches to Assess Heart Attack Risk after COVID-19: A Comparative Study

The Corona Virus Disease of 2019, also known as COVID-19, spread like wildfire around the planet and the leading reason of mortality worldwide after COVID-19 is Heart attack. The SARSCoV-2 virus produced pandemic. It had enormous influence on global health, affecting millions of people worldwide. Physicians and researchers took note closely to the relationship between COVID19 and cardiovascular health. As the world enters the post-pandemic phase, long-term consequences of COVID-19 on heart conditions remain a concern. Heart attack cases are increasing. It is important to predict any such dangers. This study aims to evaluate different machine learning algorithms to anticipate risk of heart attack in patients after COVID-19. Fl-score, Precision and Recall are used for evaluating each model’s performance. Various Machine learning test models are used for it. The study focuses on people who suffered from virus and having a high chance of heart attacks in future. The purpose of this research is to analyze and predict the probability of cardiac arrest in Covid-19 patients using machine learning techniques. These algorithms are included in our dataset.