Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume 104, Issue 3, Pages E75-E86Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2010.06.021
Keywords
Machine learning; Coronary artery disease; Medical diagnostics; Multi-resolution image parameterization; Association rules; Principal component analysis
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Funding
- Slovenian Ministry of Higher Education, Science, and Technology
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The paper presents results of our long-term study on using image processing and data mining methods in a medical imaging. Since evaluation of modern medical images is becoming increasingly complex, advanced analytical and decision support tools are involved in integration of partial diagnostic results. Such partial results, frequently obtained from tests with substantial imperfections, are integrated into ultimate diagnostic conclusion about the probability of disease for a given patient. We study various topics such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform medical practice. Our long-term study reveals three significant milestones. The first improvement was achieved by significantly increasing post-test diagnostic probabilities with respect to expert physicians. The second, even more significant improvement utilizes multi-resolution image parametrization. Machine learning methods in conjunction with the feature subset selection on these parameters significantly improve diagnostic performance. However, further feature construction with the principle component analysis on these features elevates results to an even higher accuracy level that represents the third milestone. With the proposed approach clinical results are significantly improved throughout the study. The most significant result of our study is improvement in the diagnostic power of the whole diagnostic process. Our compound approach aids, but does not replace, the physician's judgment and may assist in decisions on cost effectiveness of tests. (C) 2010 Elsevier Ireland Ltd. All rights reserved.
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