Development of machine learning models for diagnosis of glaucoma.
Development of machine learning models for diagnosis of glaucoma.
复制标题
DOI：
10.1371/journal.pone.0177726
复制
发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Oh S
中科院分区：
文献类型：
--
作者：
Kim SJ;Cho KJ;Oh S
该研究旨在开发基于视网膜神经纤维层（RNFL）厚度和视野（VF）诊断青光眼的具有强大预测能力和可解释性的机器学习模型。我们从视网膜神经纤维层（RNFL）厚度和视野（VF）的检查中收集各种候选特征。我们还开发了合成功能，从原始功能。然后，我们通过特征评估选择适合分类（诊断）的最佳特征。我们使用了100例数据作为测试数据集，399例数据作为训练和验证数据集。为了开发青光眼预测模型，我们考虑了四种机器学习算法：C5.0，随机森林（RF），支持向量机（SVM）和k-最近邻（KNN）。我们使用训练数据集重复构建学习模型，并使用验证数据集对其进行评估。最后，我们得到了产生最高验证准确度的最佳学习模型。我们使用几种方法分析了模型的质量。随机森林模型表现出最好的性能，C5.0，SVM和KNN模型表现出类似的准确性。在随机森林模型中，分类准确度为0.98，灵敏度为0.983，特异度为0.975，AUC为0.979。所开发的预测模型在青光眼和健康眼睛之间的分类中显示出高准确性、灵敏度、特异性和AUC。它将用于预测青光眼对未知的检查记录。临床医生可以参考预测结果，并能够做出更好的决策。我们可以联合收割机组合多个学习模型以提高预测准确性。C5.0模型包括用于预测的决策规则。它可以用来解释具体预测的原因。
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.