C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Predicting dire outcomes of patients with community acquired pneumonia
Journal of Biomedical Informatics - Special issue: Clinical machine learning
IEEE Intelligent Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Dynamic knowledge validation and verification for CBR teledermatology system
Artificial Intelligence in Medicine
BioWeka---extending the Weka framework for bioinformatics
Bioinformatics
An optimized experimental protocol based on neuro-evolutionary algorithms
Artificial Intelligence in Medicine
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Neural network predictions of significant coronary artery stenosis in men
Artificial Intelligence in Medicine
Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble
IEEE Transactions on Information Technology in Biomedicine
A fuzzy logic based-method for prognostic decision making in breast and prostate cancers
IEEE Transactions on Information Technology in Biomedicine
Integrating classification trees with local logistic regression in Intensive Care prognosis
Artificial Intelligence in Medicine
The socio-organizational age of artificial intelligence in medicine
Artificial Intelligence in Medicine
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Background: Outcome prediction for subarachnoid hemorrhage (SAH) helps guide care and compare global management strategies. Logistic regression models for outcome prediction may be cumbersome to apply in clinical practice. Objective: To use machine learning techniques to build a model of outcome prediction that makes the knowledge discovered from the data explicit and communicable to domain experts. Material and methods: A derivation cohort (n = 441) of nonselected SAH cases was analyzed using different classification algorithms to generate decision trees and decision rules. Algorithms used were C4.5, fast decision tree learner, partial decision trees, repeated incremental pruning to produce error reduction, nearest neighbor with generalization, and ripple down rule learner. Outcome was dichotomized in favorable [Glasgow outcome scale (GOS) = I-II] and poor (GOS = III-V). An independent cohort (n = 193) was used for validation. An exploratory questionnaire was given to potential users (specialist doctors) to gather their opinion on the classifier and its usability in clinical routine. Results: The best classifierwas obtained with the C4.5 algorithm. It uses only two attributes [World Federation of Neurological Surgeons (WFNS) and Fisher's scale] and leads to a simple decision tree. The accuracy of the classifier [area under the ROC curve (AUC) = 0.84; confidence interval (CI) = 0.80-0.88] is similar to that obtained by a logistic regression model (AUC = 0.86; CI = 0.83-0.89) derived from the same data and is considered better fit for clinical use.