{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T09:50:57Z","timestamp":1768816257895,"version":"3.49.0"},"reference-count":32,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vietnam J. Comp. Sci."],"published-print":{"date-parts":[[2020,5]]},"abstract":"<jats:p> One major characteristic of data is completeness. Missing data is a significant problem in medical datasets. It leads to incorrect classification of patients and is dangerous to the health management of patients. Many factors lead to the missingness of values in databases in medical datasets. In this paper, we propose the need to examine the causes of missing data in a medical dataset to ensure that the right imputation method is used in solving the problem. The mechanism of missingness in datasets was studied to know the missing pattern of datasets and determine a suitable imputation technique to generate complete datasets. The pattern shows that the missingness of the dataset used in this study is not a monotone missing pattern. Also, single imputation techniques underestimate variance and ignore relationships among the variables; therefore, we used multiple imputations technique that runs in five iterations for the imputation of each missing value. The whole missing values in the dataset were 100% regenerated. The imputed datasets were validated using an extreme learning machine (ELM) classifier. The results show improvement in the accuracy of the imputed datasets. The work can, however, be extended to compare the accuracy of the imputed datasets with the original dataset with different classifiers like support vector machine (SVM), radial basis function (RBF), and ELMs. <\/jats:p>","DOI":"10.1142\/s2196888820500098","type":"journal-article","created":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T07:45:01Z","timestamp":1580802301000},"page":"161-177","source":"Crossref","is-referenced-by-count":6,"title":["The Effects of Missing Data Characteristics on the Choice of Imputation Techniques"],"prefix":"10.1142","volume":"07","author":[{"given":"Oyekale Abel","family":"Alade","sequence":"first","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia"},{"name":"Department of Computer Science, Federal Polytechnic Bida, Nigeria"}]},{"given":"Ali","family":"Selamat","sequence":"additional","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia"},{"name":"Media & Games Center of Excellence (MAGICX), Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia"},{"name":"Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100,  Kuala Lumpur, Malaysia"},{"name":"University of Hradec Kr\u00e1lov\u00e9, Rokitanskeho 62, 500 03 Hradec Kr\u00e1lov\u00e9, Czech Republic"}]},{"given":"Roselina","family":"Sallehuddin","sequence":"additional","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia"}]}],"member":"219","published-online":{"date-parts":[[2020,3,20]]},"reference":[{"key":"S2196888820500098BIB002","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-009-0295-6"},{"key":"S2196888820500098BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/IESYS.2017.8233553"},{"key":"S2196888820500098BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsp.2009.10.001"},{"key":"S2196888820500098BIB005","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/145156"},{"key":"S2196888820500098BIB006","first-page":"5315","volume":"5","author":"Gimpy M. 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