부산대학교 도서관

Chronic Kidney Disease (CKD) is a chronic renal problem that affects the human kidney and makes it not to function properly or causes complete renal failure. It results in dialysis or causes other related diseases and reduces the quality of living. The symptoms of this disease cannot be identified in the preliminary stage. Only very lesser people are aware of this disease and can predict the symptoms at the earlier stage. However, it leads to prolonged disruption of kidney functional and finally causes it to failure and reduces the functionality completely. This can be occurred due to prolonged diabetes and also related with other diseases like Cardio-Vascular Disease (CVD). Due to the lack of awareness and inadequate prediction approaches in the preliminary stage, there is a delay in treating the patients" at the initial phase of disease. From the various literature studies, it is identified that CKD can be predicted and treated in the earlier stage using the soft-computational techniques. Earlier CKD predictor model needs to be improved with higher prediction accuracy and precision. Therefore, there is a need for a decision support system that assists the nephrologists during the time of emergency conditions. Therefore, in this research, Naive Bayes classifier is used for classification along with the Choice based Hierarchy (NB-CbH). NB classifier works effectually with huge dataset and reduces the computational complexity. The prediction rate and the severity of the disease analysis with NB are extremely higher.