Identification of Hypertension by Mining Class Association Rules from Multi-dimensional Features

Hypertension is a common cardiovascular disease, which will lead to severe complications without timely treatment. Accurate hypertension identification is essential to preventing the condition deteriorated. However, the state of art hypertension identification methods only extract features from very few aspects, and hence have limited identification accuracy. Furthermore, they only can judge whether the subjects are hypertensive or not, more meaningful information (such as, why the subjects suffer from hypertension) that can help doctors to improve their diagnosis level are absent. In this paper, we propose a class association rules-based method to identify hypertension. Particularly, its key idea is to utilize the relationship existing in multi-dimensional features to characterize hypertension pattern more effectively, in order to improve the identification performance. In addition, it can also generate a set of class association rules (CARs), which can reflect the subjects' physiological status and are proved to be useful for doctors to analyze subject's condition deeply. Experiments based on 128 subjects (61 hypertension patients and 67 healthy subjects) shows that our method outperforms the baseline methods and the accuracy, precision and recall reach 85.2%, 85.0%, and 83.6%, respectively. Additionally, a user study based on five clinicians demonstrates the utility of the generated CARs.