Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An Introduction to Neural Networks
An Introduction to Neural Networks
Machine Learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Intelligent Data Analysis of Out-of-Hospital Cardiac Arrest
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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The prognosis among patients who suffer out-of-hospital cardiac arrest is poor. Higher survival rates have been observed only in patients with ventricular fibrillation who were fortunate enough to have basic and advanced life support initiated early after cardiac arrest. The ability to predict outcomes of cardiac arrest would be useful for resuscitation chains. Levels of EtCO"2in expired air from lungs during cardiopulmonary resuscitation may serve as a non-invasive predictor of successful resuscitation and survival from cardiac arrest. Six different supervised learning classification techniques were used and evaluated. It has been shown that machine learning methods can provide an efficient way to detect important prognostic factors upon which further emergency unit actions are based.