eprintid: 495
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/04/95
datestamp: 2023-11-09 15:16:08
lastmod: 2023-11-09 15:16:08
status_changed: 2023-11-09 15:14:41
type: conference_item
metadata_visibility: show
creators_name: Setiawan, N.A.
creators_name: Venkatachalam, P.A.
creators_name: Hani, A.F.M.
title: A comparative study of imputation methods to predict missing attribute values in coronary heart disease data set
ispublished: pub
keywords: Artificial heart; Biomedical engineering; Cardiology; Classification (of information); Diseases; Heart; Nearest neighbor search; Statistical tests, Attribute values; Comparative studies; Coronary heart disease; imputation; Imputation methods; K-nearest neighbors; Receiver operating characteristics analysis; Rough set theory (RST), Rough set theory
note: cited By 5
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.
date: 2008
publisher: Springer Verlag
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-78349264542&doi=10.1007%2f978-3-540-69139-6_69&partnerID=40&md5=685440a65a1a064501d32e50350fc4ae
id_number: 10.1007/978-3-540-69139-6₆₉
full_text_status: none
publication: IFMBE Proceedings
volume: 21 IFM
number: 1
pagerange: 266-269
refereed: TRUE
isbn: 9783540691389
issn: 16800737
citation:   Setiawan, N.A. and Venkatachalam, P.A. and Hani, A.F.M.  (2008) A comparative study of imputation methods to predict missing attribute values in coronary heart disease data set.  In: UNSPECIFIED.