A comparative study of missing value imputation with multiclass classification for clinical heart failure data
Zhang, Y.; Kambhampati, C.; Davis, D. N.; Goode, K.; Cleland, J. G. F.
Dr Darryl Davis D.N.Davis@hull.ac.uk
Dr Kevin Goode K.M.Goode@hull.ac.uk
Research Systems Project Manager / Business Analyst
J. G. F. Cleland
Clinical data often contains missing values. Imputation is one of the best known schemes to overcome the drawbacks associated with missing values in data mining tasks. In this work, we compared several imputation methods and analyzed their performance when applied to different classification algorithms. A clinical heart failure data set was used in these experiments. The results showed that there is no universal imputation method that performs best for all classifiers. Some imputation-classification combinations are recommended for the processing of clinical heart failure data. © 2012 IEEE.
|Journal||Proceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012|
|Peer Reviewed||Peer Reviewed|
|Book Title||Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on|
|ISBN||9781467300247; 9781467300254; 9781467300230|
|APA6 Citation||Zhang, Y., Kambhampati, C., Davis, D. N., Goode, K., & Cleland, J. G. F. (2012). A comparative study of missing value imputation with multiclass classification for clinical heart failure data. In Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on, (2840-2844). https://doi.org/10.1109/fskd.2012.6233805|
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