Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Cross-validation and bootstrapping are unreliable in small sample classification
Pattern Recognition Letters
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A general model for continuous noninvasive pulmonary artery pressure estimation
Computers in Biology and Medicine
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Right-heart catheterization is the most accurate method for measuring pulmonary artery pressure (PAP). It is an expensive, invasive procedure, exposes patients to the risk of infection, and is not suited for long-term monitoring situations. Medical researchers have shown that PAP influences the characteristics of heart sounds. This suggests that heart sound analysis is a potential method for the noninvasive diagnosis of pulmonary hypertension. We describe the development of a prototype system, called PHD (pulmonary hypertension diagnoser), that implements this method. PHD uses patient data with machine learning algorithms to build models of how pulmonary hypertension affects heart sounds. Data from 20 patients were used to build the models and data from another 31 patients were used as a validation set. PHD diagnosed pulmonary hypertension in the validation set with 77% accuracy and 0.78 area under the receiver-operating-characteristic curve.