Abstract
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.
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.
| Original language | English |
|---|---|
| Title of host publication | 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery |
| Publisher | IEEE |
| Pages | 2840 - 2844 |
| ISBN (Electronic) | 978-1-4673-0024-7 |
| ISBN (Print) | 978-1-4673-0025-4 |
| DOIs | |
| Publication status | Published - 2012 |
Keywords
- missing value
- imputation
- classification
- clinical data
- heart failure