A Minimization Approach to Propositional Inductive Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
A Decision-Support System for the Identification, Staging, Functional Evaluation of Liver Diseases
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
A Model-Based Approach for Learning to Identify Cardiac Arrhythmias
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Combining Unsupervised and Supervised Machine Learning
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
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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.