Evaluating the effect of optimized cutoff values in the assessment of prognostic factors
Computational Statistics & Data Analysis
Machine Learning
Noisy replication in skewed binary classification
Computational Statistics & Data Analysis
An algorithm for nonmetric discriminant analysis
Computational Statistics & Data Analysis
Supervised classification with structured class definitions
Computational Statistics & Data Analysis
An iterated classification rule based on auxiliary pseudo-predictors
Computational Statistics & Data Analysis
Machine Learning
Editorial: Lessons learnt from bringing knowledge-based decision support into routine use
Artificial Intelligence in Medicine
A sequential neural network model for diabetes prediction
Artificial Intelligence in Medicine
Predicting glaucomatous visual field deterioration through short multivariate time series modelling
Artificial Intelligence in Medicine
Measuring overlap in binary regression
Computational Statistics & Data Analysis
Variable selection in discriminant analysis: measuring the influence of individual cases
Computational Statistics & Data Analysis
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
Empirical characterization of random forest variable importance measures
Computational Statistics & Data Analysis
Bootstrap estimated true and false positive rates and ROC curve
Computational Statistics & Data Analysis
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
Artificial Intelligence in Medicine
Bundling classifiers by bagging trees
Computational Statistics & Data Analysis
Hierarchical detection of multiple organs using boosted features
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Local decision bagging of binary neural classifiers
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Glaucoma Classification Model Based on GDx VCC Measured Parameters by Decision Tree
Journal of Medical Systems
Ensemble classification of paired data
Computational Statistics & Data Analysis
Customer churn prediction by hybrid model
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Improving bagging performance through multi-algorithm ensembles
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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Diagnosis based on medical image data is common in medical decision making and clinical routine. We discuss a strategy to derive a classifier with good performance on clinical image data and to justify the properties of the classifier by an adapted simulation model of image data. We focus on the problem of classifying eyes as normal or glaucomatous based on 62 routine explanatory variables derived from laser scanning images of the optic nerve head. As learning sample we use a case-control study of 98 normal and 98 glaucomatous subjects matched by age and sex. Aggregating multiple unstable classifiers allows substantial reduction of misclassification error in many applications and bench mark problems. We investigate the performance of various classifiers for the clinical learning sample as well as for a simulation model of eye morphologies. Bagged classification trees (bagged-CTREE) are compared to single classification trees and linear discriminant analysis (LDA). We additionally compare three estimators of misclassification error: 10-fold cross-validation, the 0.632+ bootstrap and the out-of-bag estimate. In summary, the application of our strategy of a knowledge-based decision support shows that bagged classification trees perform best for glaucoma classification.