title: On mining incomplete medical datasets: Ordering imputation and classification.
abstract: BACKGROUND
  To collect medical datasets, it is usually the case that a number of data samples contain some missing values. Performing the data mining task over the incomplete datasets is a difficult problem. In general, missing value imputation can be approached, which aims at providing estimations for missing values by reasoning from the observed data. Consequently, the effectiveness of missing value imputation is heavily dependent on the observed data (or complete data) in the incomplete datasets.
  
  
  OBJECTIVE
  In this paper, the research objective is to perform instance selection to filter out some noisy data (or outliers) from a given (complete) dataset to see its effect on the final imputation result. Specifically, four different processes of combining instance selection and missing value imputation are proposed and compared in terms of data classification.
  
  
  METHODS
  Experiments are conducted based on 11 medical related datasets containing categorical, numerical, and mixed attribute types of data. In addition, missing values for each dataset are introduced into all attributes (the missing data rates are 10%, 20%, 30%, 40%, and 50%). For instance selection and missing value imputation, the DROP3 and k-nearest neighbor imputation methods are employed. On the other hand, the support vector machine (SVM) classifier is used to assess the final classification accuracy of the four different processes.
  
  
  RESULTS
  The experimental results show that the second process by performing instance selection first and imputation second allows the SVM classifiers to outperform the other processes.
  
  
  CONCLUSIONS
  For incomplete medical datasets containing some missing values, it is necessary to perform missing value imputation. In this paper, we demonstrate that instance selection can be used to filter out some noisy data or outliers before the imputation process. In other words, the observed data for missing value imputation may contain some noisy information, which can degrade the quality of the imputation result as well as the classification performance.
authors.affiliation: Department of Pharmacy, Kaohsiung Municipal Chinese Medical Hospital, Taiwan.
authors.forename: Chih-Wen
authors.initials: CW
authors.lastname: Chen
authors.affiliation: Department of Computer Science and Information Engineering, Hwa Hsia University of Technology, Taiwan.
authors.forename: Wei-Chao
authors.initials: WC
authors.lastname: Lin
authors.affiliation: Department of Information and Computer Engineering, Chung Yuan Christian University, Taiwan.
authors.forename: Shih-Wen
authors.initials: SW
authors.lastname: Ke
authors.affiliation: Department of Information Management, National Central University, Taiwan.
authors.forename: Chih-Fong
authors.initials: CF
authors.lastname: Tsai
authors.affiliation: Department of Information Management, National Chung Cheng University, Taiwan.
authors.forename: Ya-Han
authors.initials: YH
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D000068598:Data Accuracy; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods* / Q000592:standards*; D006801:Humans; D060388:Support Vector Machine
keywords: Instance selection; incomplete data; medical data mining; missing value imputation