Data from: Development of machine learning models for diagnosis of glaucoma

The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features from the examination of retinal nerve fiber layer (RNFL) thickness and visual field (VF). We also developed synthesized features from original features. We then selected the best features proper for classification (diagnosis) through feature evaluation. We used 100 cases of data as a test dataset and 399 cases of data as a training and validation dataset. To develop the glaucoma prediction model, we considered four machine learning algorithms: C5.0, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). We repeatedly composed a learning model using the training dataset and evaluated it by using the validation dataset. Finally, we got the best learning model that produces the highest validation accuracy. We analyzed quality of the models using several measures. The random forest model shows best performance and C5.0, SVM, and KNN models show similar accuracy. In the random forest model, the classification accuracy is 0.98, sensitivity is 0.983, specificity is 0.975, and AUC is 0.979. The developed prediction models show high accuracy, sensitivity, specificity, and AUC in classifying among glaucoma and healthy eyes. It will be used for predicting glaucoma against unknown examination records. Clinicians may reference the prediction results and be able to make better decisions. We may combine multiple learning models to increase prediction accuracy. The C5.0 model includes decision rules for prediction. It can be used to explain the reasons for specific predictions.

本研究旨在开发兼具优异预测性能与可解释性的机器学习模型，用于基于视网膜神经纤维层（retinal nerve fiber layer, RNFL）厚度与视野（visual field, VF）检查结果的青光眼诊断。研究团队从视网膜神经纤维层厚度与视野检查中采集了多组候选特征，并基于原始特征构建了合成特征。随后通过特征评估筛选出适用于分类（诊断）任务的最优特征。本研究将100例数据划分为测试集，399例数据划分为训练与验证集。为构建青光眼预测模型，本研究选用了四类机器学习算法：C5.0、随机森林（random forest, RF）、支持向量机（support vector machine, SVM）以及k近邻（k-nearest neighbor, KNN）。研究通过训练集反复构建学习模型，并借助验证集开展性能评估，最终得到验证准确率最高的最优学习模型。本研究采用多项指标对各模型的性能进行分析。结果显示，随机森林模型的综合性能最优，C5.0、支持向量机与k近邻模型的准确率较为接近。其中随机森林模型的分类准确率为0.98，灵敏度为0.983，特异度为0.975，曲线下面积（area under curve, AUC）为0.979。所开发的预测模型在青光眼与健康眼的分类任务中展现出优异的准确率、灵敏度、特异度及曲线下面积，可用于未知眼科检查记录的青光眼预测。临床医生可参考该预测结果，制定更为精准的诊疗决策。后续可通过融合多类学习模型进一步提升预测准确率。C5.0模型包含用于预测的决策规则，可用于解释特定预测结果的生成依据。