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Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

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posted on 30.06.2021, 03:44 by Fan Liu, Xingshe Zhou, Zhu Wang, Jinli CaoJinli Cao, Hua Wang, Yanchun Zhang
Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients’ condition. For example, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient’s condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject’s physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively.

Funding

This work was supported by the National Natural Science Foundation of China (No. 61332013, No. 61672161), the National Key Research and Development Program of China (No. 2016YFB1001400), and the China Scholarship Council (No. 201706290110).

History

Publication Date

27/03/2019

Journal

Sensors

Volume

19

Issue

7

Article Number

1489

Pagination

25p.

Publisher

MDPI

ISSN

1424-8220

Rights Statement

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