Email updates

Keep up to date with the latest news and content from BMC Medical Informatics and Decision Making and BioMed Central.

Open Access Research article

A genetic fuzzy system for unstable angina risk assessment

Wei Dong1, Zhengxing Huang2*, Lei Ji3 and Huilong Duan2

Author Affiliations

1 Cardiology Department of Chinese PLA General Hospital, Beijing, China

2 College of Biomedical Engineering and Instrument Science, Zhejiang University, 310008, Zhou Yiqing Building 510, Zheda road 38#, Hangzhou, Zhejiang, China

3 IT Department of Chinese PLA General Hospital, Beijing, China

For all author emails, please log on.

BMC Medical Informatics and Decision Making 2014, 14:12  doi:10.1186/1472-6947-14-12

Published: 18 February 2014



Unstable Angina (UA) is widely accepted as a critical phase of coronary heart disease with patients exhibiting widely varying risks. Early risk assessment of UA is at the center of the management program, which allows physicians to categorize patients according to the clinical characteristics and stratification of risk and different prognosis. Although many prognostic models have been widely used for UA risk assessment in clinical practice, a number of studies have highlighted possible shortcomings. One serious drawback is that existing models lack the ability to deal with the intrinsic uncertainty about the variables utilized.


In order to help physicians refine knowledge for the stratification of UA risk with respect to vagueness in information, this paper develops an intelligent system combining genetic algorithm and fuzzy association rule mining. In detail, it models the input information’s vagueness through fuzzy sets, and then applies a genetic fuzzy system on the acquired fuzzy sets to extract the fuzzy rule set for the problem of UA risk assessment.


The proposed system is evaluated using a real data-set collected from the cardiology department of a Chinese hospital, which consists of 54 patient cases. 9 numerical patient features and 17 categorical patient features that appear in the data-set are selected in the experiments. The proposed system made the same decisions as the physician in 46 (out of a total of 54) tested cases (85.2%).


By comparing the results that are obtained through the proposed system with those resulting from the physician’s decision, it has been found that the developed model is highly reflective of reality. The proposed system could be used for educational purposes, and with further improvements, could assist and guide young physicians in their daily work.

Unstable angina risk assessment; Fuzzy association rule mining; Genetic algorithm