Kidney Disease: Early-Stage Identification and Prevention Using Supervised Machine Learning Techniques

Chronic Kidney Disease (CKD) is a long-term medical condition where the kidneys gradually lose their function over time. The main function of the kidneys is to filter waste products and excess fluids from the blood, which are then excreted from the body as urine. CKD is a featureless disorder of the kidney that continues for years without any major symptoms. When people go through a laboratory test for kidney disease then only its chances of detection is possible. There has been a rapid increase in the number of people who suffer from CKD in India over the past few years. For this reason, early diagnosis and effective treatment is essential. In this research study authors use various machine learning techniques such as K-Nearest Neighbour (KNN), Decision Tree (DT), Naive Bayesian Classifier (NB), and Supreme Boosting Classifier (SB) for timely detection and prediction of CKD. The authors used 400 samples from University of California, Irvine (UCI) dataset having 25 attributes of CKD [18]. The results of the study suggest that Supreme boosting classifiers have 99% accuracy which is the greatest among other classifiers used in study.