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Diagnostic processes for many diseases are becoming increasingly complex. Many results, obtained from tests with substantial imperfections, must be integrated into a diagnostic conclusion about the probability of disease in a given patient. A practical approach to this problem is to estimate the pretest probability of disease, and the sensitivity and specificity of different diagnostic tests. With this information, test results can be analyzed by sequential use of Bayes' theorem of conditional probability. The calculated posttest probability is then used as a pretest probability for the next test. This results in a series of tests, where each test is performed independently. Its results may be interpreted with or without any knowledge of other test results. By using Machine Learning techniques for test result evaluation, this process can be almost completely automated. The computer can learn from previously diagnosed patients and apply the acquired knowledge to new patients. Different Machine Learning methods can be used for evaluation of each test result. They may assist the physician as a powerful tool for assistance in pretest probability estimation, interpretation of individual test results and in the final decision making. The presented approach has been successfully evaluated in practice in the problem of clinical diagnosis of coronary artery disease.