Wiggins M, Saad A, Litt B, Vachtsevanos G
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Appl Soft Comput. 2008 Jan;8(1):599-608. doi: 10.1016/j.asoc.2007.03.009.
To classify patients by age based upon information extracted from their electro-cardiograms (ECGs). To develop and compare the performance of Bayesian classifiers. METHODS AND MATERIAL: We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Continuous signal feature variables are converted to a discrete symbolic form by thresholding, to lower the dimensionality of the signal. This simplifies calculation of conditional probability tables for the classifier, and makes the tables smaller. Two methods of network discovery from data were developed and compared: the first using a greedy hill-climb search and the second employed evolutionary computing using a genetic algorithm (GA). RESULTS AND CONCLUSIONS: The evolved Bayesian network performed better (86.25% AUC) than both the one developed using the greedy algorithm (65% AUC) and the naïve Bayesian classifier (84.75% AUC). The methodology for evolving the Bayesian classifier can be used to evolve Bayesian networks in general thereby identifying the dependencies among the variables of interest. Those dependencies are assumed to be non-existent by naïve Bayesian classifiers. Such a classifier can then be used for medical applications for diagnosis and prediction purposes.
根据从心电图（ECG）中提取的信息对患者进行年龄分类。开发并比较贝叶斯分类器的性能。方法和材料：我们提出了一种使用遗传进化贝叶斯网络分类器根据从ECG信号中提取的统计特征对患者进行分类的方法。通过阈值处理将连续信号特征变量转换为离散符号形式，以降低信号的维度。这简化了分类器条件概率表的计算，并使表更小。开发并比较了两种从数据中发现网络的方法：第一种使用贪婪爬山搜索，第二种使用遗传算法（GA）进行进化计算。结果与结论：进化后的贝叶斯网络（曲线下面积[AUC]为86.25%）比使用贪婪算法开发的网络（AUC为65%）和朴素贝叶斯分类器（AUC为84.75%）表现更好。一般来说，进化贝叶斯分类器的方法可用于进化贝叶斯网络，从而识别感兴趣变量之间的依赖关系。朴素贝叶斯分类器假定这些依赖关系不存在。这样的分类器随后可用于医疗应用中的诊断和预测目的。
