Abstract
A set of machine learning experiments was performed on the atherosclerotic coronary heart disease data collected in the regular medical practice, with intention to evaluate established medical diagnostic practice, investigate the relations between the results of less important diagnostic tests, and detect novel regularities in patients' data. The results of these experiments were evaluated by the medical expert for their soundness with respect to the existing knowledge in the domain, and potential for the generation of new and useful medical knowledge. The performed experiments generally verify the existing medical practice in non-invasive diagnosis of atherosclerotic coronary heart disease, but also demonstrate how the synergy of medical and machine learning expertise helps in the inference of a new knowledge and potentially could increase efficiency and reliability of the medical diagnostic process.
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