Post-COVID Cardiovascular Risk Assessment Using an Efficient Machine Learning Model

Individuals who were exposed to COVID-19 in different parts of their bodies, including heart attacks, faced major consequences. The frequency of cardiovascular diseases is increasing in the healthcare industry. This illness most commonly affects people aged 20 to 48. In supervised learning, data mining mainly depends on categorization algorithms. Using various combinations of calculations and algorithms, the information obtained from hospital data analysis is used for early-stage prediction of post-Covid cardiovascular disease. Machine learning is one of the strange inventions that have been widely used in a variety of fields, including the application of medical services to disease prediction. We examined the accuracy of machine learning algorithms that can be used in this study to predict heart disease analyzes and predict overall risk. One of the most current data mining methods is classification. The classification method’s primary goal is to keep the data in the correct class. In this work, classification methods such as Logistic Regression Classifier, Naive-Bayes, and Ensemble Cardiovascular Prediction Classification Algorithm were used (ECVDPCA) and the dataset is taken from Kaggle repository. The goal of this study is to find the most exact classification approach and to improve accuracy in predicting Post-Covid Cardio-Vascular illness. Python was used to get the ideal solution in this case to predict the accuracy of the classifier in which the proposed classifier gave the best accuracy when compared with the other two classifiers i.e., 95