An Effective Way to Identify Chronic Kidney Disease Using Machine Learning

Nowaday's most of the people are suffering from kidney diseases due to poor quality of food and water and also because of modern life style. There are so many kidney problems like Kidney Infection, Kidney Stones and Polycystic Kidney Disease. Chronic Kidney Disease is the major type of kidney disease where it is most urgent to identify CKD at the very initial stage so that it can be cured otherwise it poses a serious threat to life. Predicting CKD is a very challenging research problem as most of the research fails to produce accurate results. There were many kidney disease prediction systems that were developed by many researches which use classification & prediction algorithms but each of the algorithms has its own limitations. The main objective of this paper is to overcome the existing limitations and to predict the possibility of CKD disease accurately. The CKD dataset is being taken from UCI Repository and has 25 attributes is used for implementation. This work is implemented using the algorithms Random Forest, Decision Tree, SVM & KNN.