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Journal of Healthcare Engineering
Volume 2017, Article ID 6183714, 10 pages
Research Article

Noninvasive Evaluation of Portal Hypertension Using a Supervised Learning Technique

1Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
2Gastroenterology Department, Lithuanian University of Health Sciences, Kaunas, Lithuania

Correspondence should be addressed to Mindaugas Marozas; ude.utk@sazoram.m

Received 21 May 2017; Revised 31 August 2017; Accepted 25 September 2017; Published 12 October 2017

Academic Editor: Andreas Maier

Copyright © 2017 Mindaugas Marozas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Portal hypertension (PHT) is a key event in the evolution of different chronic liver diseases and leads to the morbidity and mortality of patients. The traditional reliable PHT evaluation method is a hepatic venous pressure gradient (HVPG) measurement, which is invasive and not always available or acceptable to patients. The HVPG measurement is relatively expensive and depends on the experience of the physician. There are many potential noninvasive methods to predict PHT, of which liver transient elastography is determined to be the most accurate; however, even transient elastography lacks the accuracy to be a perfect noninvasive diagnostic method of PHT. In this research, we are focusing on noninvasive PHT assessment methods that rely on selected best-supervised learning algorithms which use a wide set of noninvasively obtained data, including demographical, clinical, laboratory, instrumental, and transient elastography measurements. In order to build the best performing classification meta-algorithm, a set of 21 classification algorithms have been tested. The problem was expanded by selecting the best performing clinical attributes using algorithm-specific filtering methods that give the lowest error rate to predict clinically significant PHT. The suggested meta-algorithm objectively outperforms other methods found in literature and can be a good substitute for invasive PHT evaluation methods.