IMPROVEMENT OF CLASSIFIER ACCURACY ON CLINICAL DATA SETS BY OPTIMAL SELECTION OF IMPUTATION METHODS
DOI:
https://doi.org/10.61841/y7qb4t23Keywords:
Missing value imputation, cardiovascular data, Mean imputation, Group mean imputation, kNN imputation, Multi-Linear Regression Imputation, C5.0, Random Forest, Performance MeasuresAbstract
Missing value imputation is one of the biggest tasks of data pre-processing when whenperforming data mining. Most clinical datasets are usually incomplete. Simply removing the incomplete cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to producegood quality datasets for better analyzing clinical trials. In this paper we explore the use of a machine learning technique as a missing value imputation method for incomplete cardiovascular data. Mean imputation, group mean imputation, kNN imputation, and multi-linear regression imputation are used as missing value imputation, and the imputed datasets are subject to classification and prediction using C5.0 and random forest classifiers. The experiment shows that final classifier performance is improved when multi-linear regression imputation is used to predict missing attribute values for random forest, and in most cases, the machine learning techniques were found to perform better than the standard mean imputation technique.
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