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首页> 外文期刊>Expert systems with applications >Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease
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Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease

机译：Logistic回归，分类和回归树以及神经网络预测冠状动脉疾病的性能比较

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摘要

In this study, performances of classification techniques were compared in order to predict the presence of coronary artery disease (CAD). A retrospective analysis was performed in 1245 subjects (865 presence of CAD and 380 absence of CAD). We compared performances of logistic regression (LR), classification and regression tree (CART), multi-layer perceptron (MLP), radial basis function (RBF), and self-organizing feature maps (SOFM). Predictor variables were age, sex, family history of CAD, smoking status, diabetes mellitus, systemic hypertension, hypercholesterolemia, and body mass index (BMI). Performances of classification techniques were compared using ROC curve, Hierarchical Cluster Analysis (HCA), and Multidimensional Scaling (MDS). Areas under the ROC curves are 0.783, 0.753, 0.745, 0.721, and 0.675, respectively for MLP, LR, CART, RBF, and SOFM. MLP was found the best technique to predict presence of CAD in this data set, given its good classificatory performance. MLP, CART, LR, and RBF performed better than SOFM in predicting CAD in according to HCA and MDS.
机译：在这项研究中，比较了分类技术的性能，以预测冠状动脉疾病（CAD）的存在。对1245名受试者（865名存在CAD和380名没有CAD）进行了回顾性分析。我们比较了逻辑回归（LR），分类和回归树（CART），多层感知器（MLP），径向基函数（RBF）和自组织特征图（SOFM）的性能。预测变量包括年龄，性别，CAD家族史，吸烟状况，糖尿病，系统性高血压，高胆固醇血症和体重指数（BMI）。使用ROC曲线，层次聚类分析（HCA）和多维标度（MDS）比较了分类技术的性能。对于MLP，LR，CART，RBF和SOFM，ROC曲线下的面积分别为0.783、0.753、0.745、0.721和0.675。鉴于其良好的分类性能，MLP被认为是预测CAD在此数据集中是否存在的最佳技术。根据HCA和MDS，MLP，CART，LR和RBF在预测CAD方面表现优于SOFM。

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