Kernel similarity-based multigranulation three-way decision approach to hypertension risk assessment with multi-source and multi-level structure data

Early intervention and prevention of chronic diseases is a fundamental way to reduce disease incidence. Due to the complex pathogenic factors of disease, the assessment of disease risk requires consideration of the influence of multiple factors. In clinical practice, disease risk assessment may face data with multi-source, multi-type and multiple indicators of examination items. Mining useful information and rules in this complex information is the foundation for further decisions. However, the existing knowledge representation methods cannot comprehensively and accurately describe these characteristic of data. Therefore, we introduce the concept of multiple hybrid attribute information systems (MHAISs). Then, we defined the binary relations over MHAIS by fusing different types of kernel functions. On this basis, we construct a variable precision multigranulation kernel rough set (VPMGKRS), and propose a multigranulation three-way decision method over MHAIS. In addition, considering the individual difference between decision objects and the diversity of data characteristic among sources, we calculate the loss functions under different granular with the help of conditional probabilities, and then obtain the thresholds for the three-way decisions. Finally, we use 712 clinical random samples to conduct a simulation analysis applying the proposed method to assess the risk of hypertension. The experiment result confirmed the applicability and validity of the model.