An aided diagnosis model of sub-health based on rough set and fuzzy mathematics: A case of TCM

Article type: Research Article

Authors: Jiang, Qi-yu^* | Yang, Xiao-jing | Sun, Xiao-sheng^*

Affiliations: Guangzhou University of Chinese Medicine, Guangzhou, China

Correspondence: [*] Corresponding author. Qi-yu Jiang and Xiao-sheng Sun, Guangzhou University of Chinese Medicine, Guangzhou, China. Tel.: +86 020 39358163; E-mails: jiangqiyu@gzucm.edu.cn (Q.-Y. Jiang); sunxiaosheng@gzucm.edu.cn (X.-S. Sun).

Abstract: Sub-health is the third state featuring a deterioration in physiological function between health and illness, and it has been a global problem received increasing attention. This paper presents a novel computational model for aided diagnosis of sub-health, which is with TCM (Traditional Chinese Medicine) diagnosis as an instance. All the original medical records of sub-health were obtained from the First Affiliated Hospital of Guangzhou University of Chinese Medicine, and these records were divided into training set (training cases) and test set (test cases). Based on rough set and fuzzy mathematics, training set was used to extract important features in different classifications of sub-health and generated fuzzy weight matrixes. The results of test set were achieved with integrated calculation of fuzzy weight matrixes and feature values of sub-health symptom. In order to further evaluate the novel model, it was compared with the linear model, Naive Bayesian classification and fuzzy comprehensive. The results showed that the accuracy of the novel model for the diagnosis of sub-health is higher than the linear model and Naive Bayesian classification, and is a little better than fuzzy comprehensive. So the novel model presented in this study can be used to assist the diagnosis of sub-health and play an active role in intelligent medical inthe future.

Keywords: Aided decision, diagnosis model, fuzzy mathematics, rough set, TCM

DOI: 10.3233/JIFS-15958

Journal: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 6, pp. 4135-4143, 2017