Detecting Renal Disease using Meta-Classifiers

Abstract

Because of the elevated risk of illness and fatality, chronic renal disease is regarded as a serious health issue. Renal disease is also called kidney disease. Kidney infections are particularly challenging to diagnose since they progress slowly and continuously. For the same reason, a lot of patients wait until the very end stage to diagnose their condition. It’s critical to have trustworthy methods in the early stage of renal disease assessment. The ML (Machine Learning) approaches are crucial for illness diagnosis and early-stage diagnosis. This project’s primary goal is to evaluate the renal disease risk probability stages. It is created for classification methods that are used as meta multistage classifiers to define the danger stage. The techniques are broken up into different stages to complete the goal. The conventional data of the first module is preprocessed data. The methods used to calculate pre-processing are label encoding and standard scalar. Meta classifiers are used in extra tree classifiers to process the data along with some classifiers like K-Nearest neighbor and Random Forest. As a result, the kidney infection risk stage is known. By using meta classifiers to the Random Forest tree, a better accuracy has been obtained when compared to the existing methods.