Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
SVM in oracle database 10g: removing the barriers to widespread adoption of support vector machines
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Noninvasive diagnosis of pulmonary hypertension using heart sound analysis
Computers in Biology and Medicine
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
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Elevated pulmonary artery pressure (PAP) is a significant healthcare risk. Continuous monitoring for patients with elevated PAP is crucial for effective treatment, yet the most accurate method is invasive and expensive, and cannot be performed repeatedly. Noninvasive methods exist but are somewhat inaccurate, expensive, and cannot be used for continuous monitoring. We present a machine learning model based on heart sounds that estimates pulmonary artery pressure with enough accuracy to exclude an invasive diagnostic operation, allowing for consistent monitoring of heart condition in suspect patients without the cost and risk of invasive monitoring. We conduct a greedy search through 38 possible features using a 109-patient cross-validation to find the most predictive features. Our best general model has a standard estimate of error (SEE) of 8.3mmHg, which outperforms the previous best performance in the literature on a general set of unseen patient data.