Abstract
This research addresses the critical challenge of accurate diagnosis of kidney disease using the Fuzzy Unordered Rule Induction Algorithm (FURIA). The objectives of this study are twofold: first, to develop a novel approach leveraging FURIA models with a heightened sensitivity to severe instances in kidney disease datasets; and second, to evaluate the impact of varying the 'N' parameter, controlling the minimum weights of instances within splits, on the overall performance of the model. Motivated by the paramount importance of early and accurate detection of severe cases of kidney disease, our research proposes a methodology that integrates Gaussian membership functions tailored to the characteristics of specific kidney disease datasets. The background of the study emphasizes the significance of robust diagnostic tools in nephrology and the potential of fuzzy rule-based systems to enhance sensitivity to severe instances. Preliminary results demonstrate promising outcomes, suggesting that the proposed FURIA-KD framework effectively identifies severe cases, paving the way for improved diagnostic precision and patient outcomes in kidney disease.
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