ISI Journals

Kidney stones are a common urological disorder that affects millions of people worldwide. The analysis of kidney stones often involves the identification of the chemical composition of the stones, which can be challenging due to the complex nature of the stones. Clustering is a technique that can be used to group similar objects together based on their characteristics. Game theory provides a framework for modeling decision-making processes and has been applied to the clustering of objects in various domains. This article proposes a game theory-based clustering approach for kidney stone analysis. The literature review examines the current state of kidney stone analysis, the challenges faced by clustering techniques, and the potential benefits of game theory. The research methodology involves the development of a game theory-based clustering approach for kidney stone analysis. The results show that the proposed approach can improve the accuracy and efficiency of kidney stone analysis. The conclusion discusses the implications of these findings for future research and the potential for game theory-based approaches to improve analysis in other healthcare domains.