PERFORMANCE COMPARISON OF THREE DATA MINING TECHNIQUES FOR PREDICTING KIDNEY DIALYSIS

The main objective of this manuscript is to report on research where we took advantage of those available technological advancements to develop prediction models for kidney dialysis survivability, and also the main goal of medical data mining techniques is to get best algorithms that describe given data from multiple aspects. The number of patients on hemodialysis due to end stage kidney disease is increasing. The median survival for these patients is only about 3 years and the cost of providing care is high. Finding ways to improve patient outcomes and reduce the cost of dialysis is a challenging task. Dialysis care is complex and multiple factors may influence patient survival. More than 50 parameters may be monitored while providing a kidney dialysis treatment. Understanding the collective role of these parameters in determining outcomes for an individual patient and administering dividualized treatments is of importance. Individual patient survival may depend on a complex interrelation ship between multiple demographic and clinical variables, medications, and medical interventions. In this research, three data mining techniques (Artificial Neural Networks, Decision tree and Logical Regression) are used to elicit knowledge about the interaction between these variables and patient survival. A performance comparison of three data mining techniques is employed for extracting knowledge in the form of classification rules. The concepts introduced in this research have been applied and tested using a data collected at different dialysis sites. The computational results are reported. Finally, ANN is suggested for Kidney dialysis to get better results with accuracy and performance