https://scholars.lib.ntu.edu.tw/handle/123456789/557479
標題: | Missing value imputation on multiple measurements for prediction of liver cancer recurrence: A comparative study | 作者: | Ping, X.-O. Tseng, Y.-J. JA-DER LIANG GUAN-TARN HUANG PEI-MING YANG FEI-PEI LAI |
公開日期: | 2015 | 出版社: | IOS Press | 卷: | 274 | 起(迄)頁: | 1930-1939 | 來源出版物: | Frontiers in Artificial Intelligence and Applications | 摘要: | The problem of missing values frequently occurs during data analysis. Imputation is one of the solutions to handle missing data. Clinical data often contain multiple measurements such as laboratory test results which are measured at different time points. In this study, we compared three imputation methods and their effects on different multiple measurement data sets with different sampling time periods. Data sets of liver cancer were used in this study for classification of liver cancer recurrence based on two types of classification models built by support vector machine (SVM) and random forests. The results report appropriate combinations of imputation methods and sampling time periods which achieve better classification results than those of other imputation methods and periods. These reported the leading imputation method with SVM is significantly different (P<0.001) from mean imputation with SVM which is frequently used by data sets with missing values. ? 2015 The authors and IOS Press. All rights reserved. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926444199&doi=10.3233%2f978-1-61499-484-8-1930&partnerID=40&md5=6297328b96f9951571b4c71c43dd142a https://scholars.lib.ntu.edu.tw/handle/123456789/557479 |
ISSN: | 0922-6389 | DOI: | 10.3233/978-1-61499-484-8-1930 | SDG/關鍵字: | Autocorrelation; Classification (of information); Decision trees; Diseases; Intelligent control; Intelligent systems; Classification models; Classification results; Comparative studies; imputation; Missing value imputation; Missing values; Multiple measurements; Random forests; Support vector machines |
顯示於: | 醫學系 |
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