Influence of missing data imputation method on the classification accuracy of the medical data

• Autorzy: Orczyk T. , Porwik P.
• Języki publikacji: EN

Abstrakty

EN

Aim of this study is to show the dangers of filling missing data - particularly medical data. Because there are many dedicated medical expert systems and medical decision support systems, a special attention must be paid on the construction of classifiers. Medical data are almost never complete, and completion of the missing data requires a special care. The safest approach of dealing with missing data would be removing records with missing parameters and/or removing parameters that are missing in the records. Unfortunately reducing data set that is already very small is not always an option. Dangers coming out from data imputation are shown in the article, which presents the influence of selected missing data filling algorithms on the classification accuracy.

Słowa kluczowe

EN

medical data analysis; missing data; data imputation; classification efficiency

PL

analiza danych medycznych; brakujące dane; przypisanie danych; efektywność klasyfikacji

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2013
• Tom: Vol. 22
• Strony: 111--116
• Opis fizyczny: Bibliogr. 11 poz., tab., wykr.

Twórcy

autor

Orczyk T.

• University of Silesia

autor

Porwik P.

• University of Silesia

Bibliografia

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