A comparative study of imputation methods to predict missing attribute values in coronary heart disease data set

Abstract

The objective of this research is to investigate the effects of missing attribute value imputation methods on the quality of extracted rules when rule filtering is applied. Three imputation methods: Artificial Neural Network with Rough Set Theory (ANNRST), k-Nearest Neighbor (k-NN) and Concept Most Common Attribute Value Filling (CMCF) are applied to University California Irvine (UCI) coronary heart disease data sets. Rough Set Theory (RST) method is used to generate the rules from the three imputed data sets. Support filtering is used to select the rules. Accuracy, coverage, sensitivity, specificity and Area Under Curve (AUC) of Receiver Operating Characteristics (ROC) analysis are used to evaluate the performance of the rules when they are applied to classify the complete testing data set. Evaluation results show that ANNRST is considered as the best method among k-NN and CMCF. © 2008 Springer-Verlag.