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Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

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DOI: http://dx.doi.org/10.15439/2018F196

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 129–135 ()

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Abstract. The paper describes the study on the problem of missing values in medical data collected to discover new dependencies between parameters of children born with intrauterine growth restriction disorder. The aim of the research is to propose a procedure that may be taken to improve the medical inference in the presence of missing data. The approach with use of unconditional mean and k-nearest neighbor imputation has been applied. The experiments proved that application of missing data imputation in original dataset yields more valuable dependencies when compared to original data, maintaining the confidence interval for goodness of fit with the original distribution above 90\%. The discovered dependencies in data may establish the basis for new treatment procedures of children with intrauterine growth restriction disorder.

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