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

The paper describes the study on the problem of missing values in medical data collected to discover new dependencies between parameters in 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.