Predicting the Risk Factor of Kidney Disease using Meta Classifiers

Chronic kidney disease is considered a major health concern because of the increased risk of illness and mortality. As kidney infections are slow and chronic, they are more difficult to diagnose. This is the same reason why many patients are unable to make a diagnosis until the final stage. It is important to have reliable techniques to assess CKD at an early stage. In disease diagnosis and early-stage diagnosis, machine learning approaches play an essential role. The main objective of this project is to assess the risk phase of CKD. To determine the risk stage, this model is developed using classification techniques that are employed as meta classifiers. To complete the task, the technique is divided into various parts. In the first module, the traditional data is pre-processed. Label encoding and standard scalar are the techniques that are used to compute pre-processing. Base classifiers like KNN and Random Forest are used, Extra tree classifiers are used as meta classifiers to process the information. Therefore, the risk stage of kidney infection is known.