论文标题
冠状动脉疾病诊断；使用随机树模型对重要特征进行排名
Coronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Model
论文作者
论文摘要
心脏病是中年公民中最常见的疾病之一。在大量的心脏病中，冠状动脉疾病（CAD）被认为是一种常见的心血管疾病，死亡率很高。诊断CAD的最受欢迎的工具是使用医学成像，例如血管造影。但是，血管造影以昂贵，并且与许多副作用相关。因此，这项研究的目的是通过选择重要的预测特征来提高冠心病诊断的准确性。在这项研究中，我们提出了一种使用机器学习的集成方法。本研究使用了随机树（RT）的机器学习方法，C5.0的决策树，支持向量机（SVM），卡方自动互动检测（CHAID）的决策树（CHAID）。提出的方法显示出令人鼓舞的结果，研究证实RTS模型的表现优于其他模型。
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, the coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that RTs model outperforms other models.
