Summary

Objectives: Comparison of classification methods using data of one clinical study. The tuning of hyperparameters is assessed as part of the methods by nested-loop cross-validation. Methods: We assess the ability of 18 statistical and machine learning classifiers to detect glaucoma. The training data set is one case-control study consisting of confocal scanning laser ophthalmoscopy measurement values from 98 glaucoma patients and 98 healthy controls. We compare bootstrap estimates of the classification error by the Wilcoxon signed rank test and box-plots of a bootstrap distribution of the estimate. Results: The comparison of out-of-bag bootstrap estimators of classification errors is assessed by Spearman’s rank correlation, Wilcoxon signed rank tests and box-plots of a bootstrap distribution of the estimate. The classification methods random forests 15.4%, support vector machines 15.9%, bundling 16.3% to 17.8%, and penalized discriminant analysis 16.8% show the best results. Conclusions: Using nested-loop cross-validation we account for the tuning of hyperparameters and demonstrate the assessment of different classifiers. We recommend a block design of the bootstrap simulation to allow a statistical assessment of the bootstrap estimates of the misclassification error. The results depend on the data of the clinical study and the given size of the bootstrap sample.