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.