Marateb Hamid Reza, Goudarzi Sobhan
Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
J Res Med Sci. 2015 Mar;20(3):214-23.
Coronary heart diseases/coronary artery diseases (CHDs/CAD), the most common form of cardiovascular disease (CVD), are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules.
In this study, the Cleveland CAD dataset from the University of California UCI (Irvine) was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC) whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS) methods, multiple logistic regression (MLR) and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS) was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier.
In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard) for 272 subjects (68% male) were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (α) and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were "age and ST/heart rate slope categories," "exercise-induced angina status," fluoroscopy, and thallium-201 stress scintigraphy results.
The proposed method showed "substantial agreement" with the gold standard. This algorithm is thus, a promising tool for screening CAD patients.
冠心病/冠状动脉疾病（CHD/CAD）是心血管疾病（CVD）最常见的形式，是发展中国家/发达国家死亡和残疾的主要原因。医生可以检测CAD风险因素，以预防不久将来CAD的发生。侵入性冠状动脉造影作为当前的诊断方法，成本高昂且与CAD患者的发病率和死亡率相关。本研究的目的是设计一种具有临床可解释规则的基于计算机的非侵入性CAD诊断系统。
在本研究中，使用了来自加利福尼亚大学欧文分校（Irvine）的克利夫兰CAD数据集。区间尺度变量被离散化，切点取自文献。然后基于神经模糊分类器（NFC）制定了基于模糊规则的系统，其学习过程通过缩放共轭梯度算法加速。使用两种特征选择（FS）方法，多元逻辑回归（MLR）和顺序FS，来减少所需的属性。然后在留出验证框架中评估NFC（有/无FS）的性能。对最佳分类器进行了进一步的交叉验证。
在该数据集中，分析了272名受试者（68%为男性）的16个完整属性以及二元CHD诊断（金标准）。MLR+NFC表现最佳。其总体敏感性、特异性、准确性、I型错误（α）和统计功效分别为79%、89%、84%、0.1和79%。选定的特征为“年龄和ST/心率斜率类别”、“运动诱发心绞痛状态”、荧光透视以及铊-201负荷闪烁扫描结果。
所提出的方法与金标准显示出“高度一致性”。因此，该算法是筛查CAD患者的一个有前景的工具。
