Prediction for chronic kidney disease by categorical and non_categorical attributes using different machine learning algorithms

Abstract

Chronic kidney disease (CKD) is a common disease as it is difficult to diagnose early due to its lack of symptoms. The main goal is to first diagnose kidney failure, which is a requirement for dialysis or a kidney transplant. This model teaches patients how to live a healthy life, helps doctors identify the risk and severity of disease, and how plan future treatments. Machine learning algorithms are often used in health care to predict and manage the disease. The purpose of this study is to develop a model for the early detection of CKD, which has three parts: (a) applying baseline classifiers on categorical attributes, (b) applying baseline classifiers on non_categorical attributes, (c) applying baseline classifiers on both categorical and non_categorical attributes, and (d) improving the results of the proposed model by combing the results of above three classifiers based on a majority vote. The proposed model based on baseline classifiers and the majority voting method shows a 3% increase in accuracy over the other existing models. The results provide support for increased accuracy in the current classification of chronic kidney disease.