Kernel alignment-based three-way clustering on attribute space and its application in stroke risk identification

Abstract

Identifying the key risk factors of disease from a large amount of clinical data is a prerequisite for further scientific decisionmaking. In medical practice, the clinical symptom information of patients usually includes various types of data. Meanwhile, the occurrence and development of diseases are joint result of the mutual influence factors. Therefore, there is usually a correlation between attributes. In this paper, we discuss a kind of hybrid attribute feature selection problem considering the correlation between attributes. Firstly, we take the identification of disease pathogenic factors in medical decision as the background, and construct a hybrid attribute decision system. Secondly, by introducing kernel alignment, the uncertain relationship between attributes is defined. Based on this, a three-way clustering model in attribute space is established. Furthermore, a feature selection method for hybrid attribute data based on three-way clustering in attribute space is proposed. Finally, we applied the proposed model to identify the pathogenic factors of stroke and used 279 clinical random samples for simulation analysis. The results verified the applicability and validity of the model. The main contributions of this paper include two aspects. In terms of theory, by introducing kernel alignment, a three-way clustering algorithm in attribute space is established. Meanwhile, a hybrid attribute feature selection method based on three-way clustering is proposed. In terms of application, the proposed method is applied to identify risk factors of stroke.