A Machine Learning Approach to Predict Renal Diseases with SARS-CoV-2

Research has shown that up to a lot of people hospitalized with COVID-19 get an intense kidney injury. In some serious cases, Kidney failure occurs suddenly without any major symptoms that are totally unpredictable to identify in the early stage. The reason behind that we have a lack of knowledge and experience regarding this. The main purpose of our research is to develop a framework that will assist individuals with foreseeing the danger of constant renal sickness growing rate after being infected with COVID-19. Here we have utilized 773 raw data and trained them and we have also taken care of our missing data. In this paper, we have used KNN, Naïve Bayes, ANN model and Ant Colony Optimization (ACO) for making the system ready for assumption. We have carried out these calculations in the python language. The exactness that we acquire by utilizing KNN calculation is 95%, Naïve bayes is 98.30% ANN is 97.5% and Ant Colony Optimization (ACO) is 95.5% separately which is generally outstanding. By utilizing our proposed strategy, prediction of renal diseases after COVID-19 in the beginning phase will be conceivable. All the data are collected from our neighborhood medical clinic. This research has shown us the current situation in this COVID-19 pandemic with regards to Chronic Kidney Sickness which is known as renal disease.