The implementation of z-numbers in fuzzy clustering algorithm for wellness of chronic kidney disease patients

Abstract

Abstract By gleaning insights from the data, fuzzy clustering capable to learn from data, identify patterns and make decision with minimum human intervention. However, it cannot simply study in detail regarding the quality of data, particularly knowledge of human being. Since the data are collected through decision-makers, the quality and human knowledge of the particular data are crucial factors to be considered. Compared to classical fuzzy numbers, z-numbers has ability to describe the human knowledge because it has both restraint and reliability part in its definition. Consequently, the implementation of z-numbers in fuzzy clustering algorithm is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in uncertain information development. Thus, there are two objectives of this paper; (i) to propose a reliable fuzzy clustering algorithm using z-numbers and; (ii) to cluster the Chronic Kidney Disease (CKD) patients based on the selected indicators to identify which cluster the patients belongs to (Cluster 0, Cluster 1, Cluster 2, Cluster 3 or Cluster 4) based on the membership functions defined. A case study of the CKD patients with the selected indicators is considered to demonstrate the capability of z-numbers to handle the knowledge of human being and uncertain information and also will present the idea in developing a robust and reliable fuzzy clustering algorithm particularly in dealing with knowledge of human being using z-numbers.