Missing Data Analysis Using Multiple Imputation in Relation to Parkinson's Disease

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Title: Missing Data Analysis Using Multiple Imputation in Relation to Parkinson's Disease
Authors: Houari, RimaBounceur, AhcèneKechadi, Taharet al.
Permanent link: http://hdl.handle.net/10197/10849
Date: 11-Nov-2016
Online since: 2019-07-03T11:12:23Z
Abstract: Missing data is an omnipresent problem in neurological control diseases, such as Parkinson's Disease. Statistical analyses on the level of Parkinson's Disease may be not accurate, if no adequate method for handling missing data is applied. In order to determine a useful way to treat missing data on Parkinson's stage, we propose a multiple imputation method based on the theory of Copulas in the data pre-processing phase of the data mining process. Our goal to use the theory of Copulas is to estimate the multivariate joint probability distribution without constraints of specific types of marginal distributions of random variables that represent the dimensions of our datasets. To evaluate the proposed approach, we have compared our algorithm with seven state-of-the-art imputation methods such as mean, regression, min, max, K-nearest neighbors, Markov Chain Monte Carlo, Expected Maximization methods, on the basis of six dataset cases containing 5%, 15%, 25%, 35%, 45% and 50% missing data percentages, respectively. The accuracy of each imputation method was evaluated using the Root Mean Square Error (RMSE) formula. Our results indicate that the proposed method outperforms significantly the existing algorithms.
Funding Details: Science Foundation Ireland
metadata.dc.description.othersponsorship: Insight Research Centre
Type of material: Conference Publication
Publisher: ACM
Series/Report no.: ACM International Conference Proceeding Series
Copyright (published version): 2016 ACM
Keywords: Data miningData pre-processingMulti-dimensional samplingCopulasMultiple imputationMissing dataParkinson’s Disease
DOI: 10.1145/3010089.3010117
Language: en
Status of Item: Peer reviewed
Is part of: Boubiche, D.E., Hamdan, H., Bounceu, A. (eds.). BDAW '16 Proceedings of the International Conference on Big Data and Advanced Wireless Technologies
Conference Details: BDAW '16: International Conference on Big Data and Advanced Wireless Technologies, Blagoevgrad, Bulgaria, 10-11 November 2016
ISBN: 978-1-4503-4779-2
Appears in Collections:Insight Research Collection

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