A worldwide epidemic of COVID-19 occurred in the year 2020, and it was unprecedented. Contributions in computer science are mostly focused on the creation of techniques for the identification, detection, and prediction of COVID-19 instances. The method most often employed in this field is machine learning (ML). Automatic methods are required for the detection of this illness, given the pace of COVID-19 dissemination. The primary function of an ML model is to forecast death rates based on various symptoms and patient conditions. Health professionals gather data from a variety of patients. The dataset includes several variables that describe the various symptoms and degrees of patient immunity. The dataset includes several variables that describe different signs and degrees of resistance associated with patients. Training the model using several strategies to estimate people’s death rates yielded the anticipated result. The data collected includes various factors such as the severity of symptoms, hypertension, and diabetes, and it also includes the count of white blood cells and platelet count as such. A model may be constructed as there is some association between these factors. The accuracy of the gradient matrices created for each technique allows us to choose the best one