Inconsistency Tests for Patient Records in a Coronary Heart Disease Database

The work presents the results of inconsistency detection experiments on the data records of an atherosclerotic coronary heart disease database collected in the regular medical practice. Medical expert evaluation of some preliminary inductive learning results have demonstrated that explicit detection of outliers can be useful for maintaining the data quality of medical records and that it might be a key for the improvement of medical decisions and their reliability in the regular medical practice. With the intention of on-line detection of possible data inconsistences, sets of confirmation rules have been developed for the database and their test results are reported in this work.