Chronic Kidney Disease analysis using data mining classification techniques
Data mining has been a current trend for attaining diagnostic results. Huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from massive dataset, categorizing valid and unique patterns in data. There are many data mining techniques like clustering, classification, association analysis, regression etc. The objective of our paper is to predict Chronic Kidney Disease(CKD) using classification techniques like Naive Bayes and Artificial Neural Network(ANN) The experimental results implemented in Rapidminer tool show that Naive Bayes produce more accurate results than Artificial Neural Network.
2016 6th International Conference Cloud System and Big Data Engineering (Confluence)