Modern parameterization and explanation techniques in diagnostic decision support system: A case study in diagnostics of coronary artery disease

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ObjectiveCoronary artery disease has been described as one of the curses of the western world, as it is one of its most important causes of mortality. Therefore, clinicians seek to improve diagnostic procedures, especially those that allow them to reach reliable early diagnoses. In the clinical setting, coronary artery disease diagnostics are typically performed in a sequential manner. The four diagnostic levels consist of evaluation of (1) signs and symptoms of the disease and electrocardiogram at rest, (2) sequential electrocardiogram testing during the controlled exercise, (3) myocardial perfusion scintigraphy, and (4) finally coronary angiography, that is considered as the “gold standard” reference method. Our study focuses on improving diagnostic performance of the third, virtually non-invasive, diagnostic level.

论文关键词:Machine learning,Multi-resolution image parameterization,Association rules,Principal component analysis,Coronary artery disease diagnostics

论文评审过程:Received 9 March 2011, Revised 10 April 2011, Accepted 17 April 2011, Available online 8 June 2011.

论文官网地址:https://doi.org/10.1016/j.artmed.2011.04.009