engleski

Noise elimination applied in early diagnosis of rheumatic diseases

Machine learning methods can be used to induce diagnostic rules from patient records with known diagnoses. In a medical application it is crucial that a machine learning system is capable of detecting regularities in the data by appropriately dealing with imperfect data, i.e., data that contains various kinds of errors, either random or systematic. The paper presents a compression-based method that is capable of detecting data which is suspected to contain errors and is therefore unsuited for the extraction of regularities genuine to this dataset. This noise elimination method is applied to a problem of early diagnosis of rheumatic diseases which is known to be difficult due to its nature and to the imperfections in the available dataset. The method is evaluated by applying the noise elimination algorithm in conjunction with the CN2 rule induction algorithm, and by comparing their performance to earlier results obtained by CN2 in this diagnostic domain.

machine learning; noise detection; rheumatology

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