Track: COVID-19 Analytics Competition

Abstract

The COVID-19 outbreak has brought about major health crises globally and the persistent nature of long COVID has put a significant strain on hospitals as a result of scarce healthcare resources. Heart problems are discovered to be the most prevalent among persons who have recovered from COVID-19 among several long-lasting effects (Gupta et al., 2021). There are so many cases of what is generally occurring with post-COVID patients. This work tries to find out the proper machine learning methods to predict the post-COVID complexities which can provide the highest accuracy. The true objective of this endeavor is to conduct additional machine learning-based research in this area of medical science and to develop an accurate machine-learning model. In the case of COVID patients, there are a lot of outcomes in a post-COVID condition. Proper problem definition, data acquisition, Model selection, evaluation of training and testing models, etc., are also significant obstacles to constructing a decent prediction model. There were proposed machine learning models according to previous research which model is combined with multiple models. This research might contribute to making further approaches to predicting the complexities of other diseases like COVID-19.