Exploring Early Glaucoma and the Visual Field Test: Classification and Clustering Using Bayesian Networks
Exploring Early Glaucoma and the Visual Field Test: Classification and Clustering Using Bayesian Networks
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DOI：
10.1109/jbhi.2013.2289367
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发表时间：
2014-05-01
影响因子：
7.7
通讯作者：
Tucker, Allan
中科院分区：
文献类型：
--
作者：
Ceccon, Stefano;Garway-Heath, David F.;Tucker, Allan
贝叶斯网络（BN）是用于多个领域的分类和聚类的概率模型。它们处理未观察到的变量以及整合数据和专家知识的能力使其成为用于对青光眼中的眼睛功能测量进行建模的适当技术。在这项研究中，一组BN被用来同时进行早期青光眼的分类和聚类数据到疾病的不同阶段。提出了一种新的学习算法，结合聚类和准贪婪搜索。模型的分类性能在一个独立的数据集上进行评估，同时将聚类与K均值、以前的出版物和直接知识进行比较。聚类和结构学习的使用使得能够探索疾病的视野模式，同时在诊断前（50%的灵敏度和90%的特异性）和诊断后（85%的灵敏度和90%的特异性）数据上都获得了良好的结果。所获得的分组具有深刻的见解，符合该领域的综合知识。
Bayesian networks (BNs) are probabilistic models used for classification and clustering in several fields. Their ability to deal with unobserved variables and to integrate data and expert knowledge make them an appropriate technique for modeling eye functionality measurements in glaucoma. In this study, a set of BNs is used to simultaneously perform classification of early glaucoma and cluster data into different stages of disease. A novel learning algorithm that combines clustering and quasi-greedy search is also proposed. The classification performances of the models are evaluated on an independent dataset, while the clusters are compared to K-means, previous publications, and direct knowledge. The use of clustering and structure learning enabled the exploration of the visual field patterns of the diseasewhile obtaining good results both on pre-(50% sensitivity at 90% specificity) and post-(85% sensitivity at 90% specificity) diagnosis data. Clusters obtained were insightful and in conformity with consolidated knowledge in the field.