Three-way dynamic clustering algorithms based on generalized neighborhood relations in incomplete hybrid information systems with applications in medical decision-making

Abstract

In addressing clinical challenges related to chronic diseases, the effective use of medical data in decision-making is often hindered by issues such as incompleteness, heterogeneity, and the need for continuous updates. To cope with these challenges, this study introduces a three-way dynamic clustering strategy built upon generalized neighborhood relations, aiming to enhance clustering robustness, strengthen the model’s ability to manage uncertainty, and support adaptability to dynamically evolving data. First, generalized neighborhood relations are constructed in incomplete hybrid information systems. An evaluation function is defined from two perspectives: the number of similar attributes between objects and the distance between objects, thereby optimizing similarity measurement and accurately characterizing the data structure. Second, three-way decision rules are introduced to effectively handle uncertainty in objects while maintaining classification accuracy, thereby improving the interpretability and adaptability of the clustering model. Furthermore, to accommodate the dynamic nature of medical data, a dynamic incremental clustering method based on neighborhood information is proposed to ensure that newly added patient data can be efficiently integrated into existing clusters, enhancing model real-time performance and computational efficiency. Experiments conducted on real clinical data from Chronic kidney disease (CKD) patients validate the proposed method. The results demonstrate that, compared to existing clustering algorithms, the proposed method outperforms in terms of F1-score and Rand Index evaluation metrics. It also exhibits higher applicability in patient classification, core and boundary domain partitioning, and dynamic data processing, providing effective support for precision stratified management of chronic disease patients and intelligent medical decision-making.