论文标题
使用模糊认知图诊断冠状动脉疾病的非侵入性建模方法
Non-invasive modelling methodology for the diagnosis of Coronary Artery Disease using Fuzzy Cognitive Maps
论文作者
论文摘要
心血管疾病（CVD）和中风在全球范围内产生巨大的健康和经济负担。冠状动脉疾病（CAD）是最常见的心血管疾病类型。冠状动脉造影是一种侵入性治疗，也是诊断CAD的标准程序。在这项工作中，我们说明了使用模糊认知图（FCMS）预测冠状动脉疾病（CAD）的医学决策支持系统。 FCM是一种基于人类知识的有前途的建模方法，能够处理歧义和不确定性，并学习如何适应未知或不断变化的环境。新提出的MDSS是使用模糊逻辑和模糊认知图的基本概念开发的，并进行了一些调整以改善结果。提出的模型在303名患者的标记的CAD数据集上进行了测试，获得了78.2％的精度，以击败几种最先进的分类算法。
Cardiovascular Diseases (CVD) and strokes produce immense health and economic burdens globally. Coronary Artery Disease (CAD) is the most common type of cardiovascular disease. Coronary Angiography, which is an invasive treatment, is also the standard procedure for diagnosing CAD. In this work, we illustrate a Medical Decision Support System for the prediction of Coronary Artery Disease (CAD) utilizing Fuzzy Cognitive Maps (FCMs). FCMs are a promising modeling methodology, based on human knowledge, capable of dealing with ambiguity and uncertainty, and learning how to adapt to the unknown or changing environment. The newly proposed MDSS is developed using the basic notions of Fuzzy Logic and Fuzzy Cognitive Maps, with some adjustments to improve the results. The proposed model, tested on a labelled CAD dataset of 303 patients, obtains an accuracy of 78.2% outmatching several state-of-the-art classification algorithms.
