Kidney Disease Detection Using Supervised Machine Learning Techniques

Abstract

Healthcare field has an immense measure of information, for processing those information certain methods are utilized. In healthcare industry, data mining plays an important part of predicting diseases. For detecting a disease, numerous of tests should be required from the patient. Kidney disease is the main organ in a human body. In any case, presently a-days Chronic Kidney Disease (CKD) is the most widely recognized issue for individuals. Today, a few individuals bite the dust in light of Chronic Kidney Disease. This disease is the most well-known and a genuine infection on the planet. The reformist loss of limit of a kidney is moreover called Chronic Kidney Disease. Machine learning is a promising methodology which helps in early diagnosis of disease and might help the professionals in decision making for diagnosis. This Dissertation analyzes data mining methods which can be utilized for predicting like kidney diseases. We compute precision of machine learning calculations for predicting kidney infection, for these algorithms are Naïve Bayesian, LR, Optimized XGB Algorithm by using dataset for training and testing and we found to better accuracy in that system. The point of this investigation is to build up a framework which may predict the kidney disease hazard level of a patient with a superior precision.