Hybrid deep-learning prediction model based on kernel multi-granularity fuzzy rough sets and its application in the diagnosis and treatment of chronic kidney disease

Abstract

In uncertain decision-making scenarios, quantitative scientific prediction models and methods can provide valuable support for making scientific decisions. However, the characteristics of hybrid attribute information may lead to a series of issues. These include difficulties in comparing and comprehensively evaluating different types of attributes, nonlinear relationships between attributes, and a lack of effective decision-support methods. To overcome these issues, this study introduces a kernel function to abstract the similarity of different attribute types and proposes a model called kernel multi-granularity fuzzy rough sets (KMGFRS). The KMGFRS model facilitates a thorough exploration and analysis of the uncertainties associated with decision objects. Additionally, an attribute reduction method based on KMGFRS is discussed to address redundant attributes in hybrid information systems. This method eliminates attributes that have a minimal influence on the decision results, simplifies the decision process, and enhances its effectiveness. This study integrates the KMGFRS and hybrid deep learning concepts to propose a novel prediction method aimed at enhancing accuracy and robustness. From the perspective of hybrid attribute information, this method can more accurately predict the unknown attributes of decision objects, thereby providing robust support for disease prediction in medical diagnostics and therapeutic decision-making. The experimental results indicated that the constructed model effectively handled uncertain decision-making scenarios involving hybrid attributes and fuzzy decision objects. It provides accurate and reliable decision support for chronic kidney disease (CKD), significantly enhancing the predictive accuracy of CKD types.