Covid 19 Survival Prediction through CNN and LSTM Deep Learning Models

The COVID-19 global epidemic, also known as the coronavirus infection, constitutes one of the most difficult issues that the world is now dealing with. SARS-COV-2, also known as the severe acute respiratory syndrome coronavirus 2, is the cause of COVID-19, a new and very infectious pulmonary illness. For identifying and controlling health hazards, public health initiatives and configuration management are crucial. In contrast to detecting, detecting, and addressing to instances, particularly contagious illness epidemics, these initiatives place a greater emphasis on counteractive action. A reliable estimation of the disease's location, dissemination, potential course, and statistical count is required to design effective preventative efforts. Accordingly, accurate quantitative prescriptive analytics is a crucial component of public health programs, primarily for contagious illnesses. Therefore, this research article outlines an effective and useful mechanism for the purpose of achieving covid-19 survival rate prediction through the use of deep learning methodologies. For this reason a number of different approaches on Covid-19 infection rate prediction are studied in this research article to achieve our approach. Our approach utilizes, Pearson correlation, K Nearest Neighbor, along with CNN-LSTM and Decision Tree to achieve covid19 survival rate prediction. The approach will be quantified in the next research paper on this topic.