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2013 | OriginalPaper | Chapter

Machine Learning-Based Missing Value Imputation Method for Clinical Datasets

Authors : M. Mostafizur Rahman, D. N. Davis

Published in: IAENG Transactions on Engineering Technologies

Publisher: Springer Netherlands

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Abstract

Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical 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 produce good quality datasets for better analysing 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/mode imputation, fuzzy unordered rule induction algorithm imputation, decision tree imputation and other machine learning algorithms are used as missing value imputation and the final datasets are classified using decision tree, fuzzy unordered rule induction, KNN and K-Mean clustering. The experiment shows that final classifier performance is improved when the fuzzy unordered rule induction algorithm is used to predict missing attribute values for K-Mean clustering and in most cases, the machine learning techniques were found to perform better than the standard mean imputation technique.

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Metadata
Title
Machine Learning-Based Missing Value Imputation Method for Clinical Datasets
Authors
M. Mostafizur Rahman
D. N. Davis
Copyright Year
2013
Publisher
Springer Netherlands
Book
IAENG Transactions on Engineering Technologies

Print ISBN: 978-94-007-6189-6

Electronic ISBN: 978-94-007-6190-2

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-94-007-6190-2_19