Prediction of Chronic Kidney Disease for Diabetes Patients using Ensemble based Machine Learning Algorithms

Abstract

The leading cause of chronic kidney disease worldwide is the diabetic kidney disease which develops approximately in forty percent of patients who are diabetic. The kidney function would gradually decrease within three months resulting in problems with total kidney function which is called as chronic kidney disease. Usually, kidney damages at early stages do not show any symptoms to the human body. Chronic Kidney disease diagnosis is usually costly, time-consuming and invasive being risky too. This is a major reason for many patients to reach the late stages without proper treatment, especially in those countries where there are limited resources. The Kidney disease could be prevented from not getting worse or bad with the right treatment. Predictive data analytics is playing a vital role in chronic management- diabetes chronic disease. In this paper Random Forest and XGBoost which are ensemble learning algorithms is used for predicting chronic kidney disease of diabetes patients