Chronic Kidney Disease and Stage Detection Using Machine Learning Classifiers

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 comes up with a set of tools and techniques which when applied to this processed data, provides knowledge to healthcare professionals for making appropriate decisions and enhancing the performance of patient management tasks. Patients with similar health issues can be grouped together and effective treatment plans could be suggested based on patient’s history, physical examination, diagnosis and previous treatment patterns. Chronic kidney disease (CKD) has become a global health issue and is an area of concern. It is a condition where kidneys become damaged and cannot filter toxic wastes in the body. The proposed system predominantly focuses on detecting chronic kidney diseases using naïve bayes and artificial neural network technique C4.5. naïve bayes is a technique used for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from finite set. The stage prediction is done using C4.5 algorithm. Decision trees can be generated using C4.5 algorithm. The decision trees generated by C4.5 are used for classification.