| Sumario: | COVID-19 and related viruses havespread around the world, posing new threats to our society. There is a clear motivation to implement protective measures that aid in the prevention of outbreaks. The effect of the COVID-19 pandemic has prompted a flood of studies aimed at better understanding, tracking, and controlling the disease. Machine learning is increasingly becoming more prevalent in the area of medical diagnosis. This can be attributed largely to advancements in disease classification and identification systems, which can provide evidence that assists medical experts in the early detection of deadly diseases, resulting in a dramatic rise in patient survival rates. In this paper, we would like to introduce a method of predicting the probability of survival of an individual infected with COVID-19, which is troubling and widely distributed in the current case scenario, using recent algorithmic improvements that were created to predict death and recovery rates.
|
|---|