A comparative study of missing value imputation with multiclass classification for clinical heart failure data

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.