Abstract

Chronic kidney disease (CKD) is a major problem on the healthcare system because of its high increasing prevalence and poor morbidity. Artificial Intelligence peculating its role in every field of research including healthcare and diagnosis of diseases. Recently, machine learning approaches are applied to raise consciousness about key health hazards including chronic kidney disease (CKD). When kidneys are damaged, they are unable to perform their normal role of filtering blood. Therefore, it is tough to anticipate, recognize, and prevent such a sickness, which may result in long-term health repercussions. Machine learning methods aid in more precise forecasting to tackle this problem at an early stage. With the increase of technology aids, it makes an ambiguity on which algorithm to apply for prediction of CKD. To address these issues several machine learning algorithms such as Logistic Regression, Naive Bayes, and Decision Tree are applied. Experiments are conducted utilizing the rich set of data in a MATLAB environment. Logistic Regression shows potential for reducing mortality from chronic kidney disease by enhancing prognosis and diagnostic accuracy at an early stage