Classification of Covid-19 High-Risk Patients Using Machine Learning

A timely and accurate diagnosis of COVID-19 is possible because of effective screening for SARS-CoV-2, which also helps to reduce the load on healthcare delivery systems. Given the limited resources available in the healthcare field, they are meant to offer support to medical staff worldwide in triaging patients, which is especially vital given the nature of the situation. It is now possible to make predictions about the risk of infection by utilizing models that consider several distinct parameters simultaneously. This paper proposes an approach to machine learning for severity detection in covid patients. This approach combines two methods, namely the bagging method and the boosting method. Additionally, this research project combines two algorithms, namely the Random Forest Algorithm and the Adaptive Boost Algorithm. The accuracy is 93%, and the performance of my machine learning model is better than the existing state-of-the-art methods.