Published Paper Details:

The field of biosciences have progressive to a higher extent and have generated large amounts of information from Electronic Health Records. This have given rise to the acute need of knowledge generation from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of biosciences. Chronic Kidney Disease (CKD) is a condition in which the kidneys are damaged and cannot filter blood as they always do. A family history of kidney diseases or failure, high blood pressure, type 2 diabetes may lead to CKD. This is a lasting damage to the kidney and chances of getting worse by time is high. The very common problems that results due to a kidney failure are heart diseases, anemia, bone diseases, high potassium, and calcium. The worst-case situation leads to complete kidney failure and necessitates kidney transplant to live. An early detection of CKD can increase the quality of life to a greater extent. This calls for good prediction algorithm to predict CKD at an earlier stage. Literature shows a wide range of machine learning algorithms employed for the prediction of CKD. This paper uses data preprocessing, data transformation and various classifiers to predict CKD and proposes best Prediction framework for CKD. The results of the framework show promising results of better prediction at an early stage of CKD.