Introduction

Visual field testing via standard automated perimetry (SAP) is a commonly used glaucoma diagnosis method. Applying machine learning techniques to the visual field test results, a valid clinical diagnosis of glaucoma solely based on the SAP data is provided. In order to reflect structural-functional patterns of glaucoma on the automated diagnostic models, we propose composite variables derived from anatomically grouped visual field clusters to improve the prediction performance. A set of machine learning-based diagnostic models are designed that implement different input data manipulation, dimensionality reduction, and classification methods.

Methods

Visual field testing data of 375 healthy and 257 glaucomatous eyes were used to build the diagnostic models. Three kinds of composite variables derived from the Garway-Heath map and the glaucoma hemifield test (GHT) sector map were included in the input variables in addition to the 52 SAP visual filed locations. Dimensionality reduction was conducted to select important variables so as to alleviate high-dimensionality problems. To validate the proposed methods, we applied four classifiers—linear discriminant analysis, naïve Bayes classifier, support vector machines, and artificial neural networks—and four dimensionality reduction methods—Pearson correlation coefficient-based variable selection, Markov blanket variable selection, the minimum redundancy maximum relevance algorithm, and principal component analysis— and compared their classification performances.

Results

For all tested combinations, the classification performance improved when the proposed composite variables and dimensionality reduction techniques were implemented. The combination of total deviation values, the GHT sector map, support vector machines, and Markov blanket variable selection obtains the best performance: an area under the receiver operating characteristic curve (AUC) of 0.912.

Conclusion

A glaucoma diagnosis model giving an AUC of 0.912 was constructed by applying machine learning techniques to SAP data. The results show that dimensionality reduction not only reduces dimensions of the input space but also enhances the classification performance. The variable selection results show that the proposed composite variables from visual field clustering play a key role in the diagnosis model.

中文翻译：

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基于标准自动视野检查数据降维的机器学习模型，用于青光眼诊断。

介绍

通过标准自动视野检查（SAP）进行视野测试是一种常用的青光眼诊断方法。将机器学习技术应用于视野测试结果，仅基于SAP数据即可提供有效的青光眼临床诊断。为了在自动诊断模型上反映青光眼的结构功能模式，我们提出了从解剖学上分组的视野簇中得出的复合变量，以提高预测性能。设计了一组基于机器学习的诊断模型，这些模型可实现不同的输入数据操作，降维和分类方法。

方法

使用375只健康眼和257只青光眼眼的视野测试数据来建立诊断模型。除了52个SAP可视字段外，从Garway-Heath映射和青光眼半场测验（GHT）扇区映射中得出的三种复合变量也包括在输入变量中。进行降维以选择重要的变量，以减轻高维问题。为了验证所提出的方法，我们应用了四个分类器（线性判别分析，朴素贝叶斯分类器，支持向量机和人工神经网络）和四个降维方法（基于皮尔逊相关系数的变量选择，马尔可夫毯式变量选择，最小冗余）最大相关度算法

结果

对于所有测试组合，实施建议的复合变量和降维技术后，分类性能得到改善。总偏差值，GHT扇区图，支持向量机和Markov覆盖变量选择的组合获得了最佳性能：接收器工作特性曲线（AUC）下的面积为0.912。

结论

通过将机器学习技术应用于SAP数据，构建了AUC为0.912的青光眼诊断模型。结果表明，降维不仅减小了输入空间的尺寸，而且提高了分类性能。变量选择结果表明，提出的视野聚类复合变量在诊断模型中起关键作用。