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논문 기본 정보

자료유형
학술저널
저자정보
저널정보
한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제10권 제3호
발행연도
2014.1
수록면
395 - 411 (17page)

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Temporal medical data is often collected during patient treatments thatrequire personal analysis. Each observation recorded in the temporal medical data isassociated with measurements and time treatments. A major problem in the analysis oftemporal medical data are the missing values that are caused, for example, by patientsdropping out of a study before completion. Therefore, the imputation of missing data isan important step during pre-processing and can provide useful information before thedata is mined. For each patient and each variable, this imputation replaces the missingdata with a value drawn from an estimated distribution of that variable. In this paper, wepropose a new method, called Newton’s finite divided difference polynomialinterpolation with condition order degree, for dealing with missing values in temporalmedical data related to obesity. We compared the new imputation method with threeexisting subspace estimation techniques, including the k-nearest neighbor, local leastsquares, and natural cubic spline approaches. The performance of each approach wasthen evaluated by using the normalized root mean square error and the statisticallysignificant test results. The experimental results have demonstrated that the proposedmethod provides the best fit with the smallest error and is more accurate than the other methods.

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