The Effects of Missing Data Characteristics on the Choice of Imputation Techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50018394" target="_blank" >RIV/62690094:18450/20:50018394 - isvavai.cz</a>

Result on the web

<a href="https://www.worldscientific.com/doi/abs/10.1142/S2196888820500098" target="_blank" >https://www.worldscientific.com/doi/abs/10.1142/S2196888820500098</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S2196888820500098" target="_blank" >10.1142/S2196888820500098</a>

Result language

angličtina

Original language name

The Effects of Missing Data Characteristics on the Choice of Imputation Techniques

Original language description

One major characteristic of data is completeness. Missing data is a significant problem in medical datasets. It leads to incorrect classification of patients and is dangerous to the health management of patients. Many factors lead to the missingness of values in databases in medical datasets. In this paper, we propose the need to examine the causes of missing data in a medical dataset to ensure that the right imputation method is used in solving the problem. The mechanism of missingness in datasets was studied to know the missing pattern of datasets and determine a suitable imputation technique to generate complete datasets. The pattern shows that the missingness of the dataset used in this study is not a monotone missing pattern. Also, single imputation techniques underestimate variance and ignore relationships among the variables; therefore, we used multiple imputations technique that runs in five iterations for the imputation of each missing value. The whole missing values in the dataset were 100% regenerated. The imputed datasets were validated using an extreme learning machine (ELM) classifier. The results show improvement in the accuracy of the imputed datasets. The work can, however, be extended to compare the accuracy of the imputed datasets with the original dataset with different classifiers like support vector machine (SVM), radial basis function (RBF), and ELMs. © 2020 The Author(s).

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Vietnam Journal of Computer Science

ISSN

2196-8888

e-ISSN

—

Volume of the periodical

7

Issue of the periodical within the volume

2

Country of publishing house

SG - SINGAPORE

Number of pages

17

Pages from-to

161-177

UT code for WoS article

000667276600004

EID of the result in the Scopus database

2-s2.0-85116030001