Missing data imputation framework for early childhood longitudinal data: a study case on NCDRC data

This research aims to develop an imputation framework for the National ChildhoodDevelopment Research Centre (NCDRC)s missing data. Missing data and other associatedissues, such as outliers, time points, noise, and continuity, were the main challenges in thisresearch. The nature of the NCDRC dataset...

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Main Author:	Al-Amoodi, Abdullah Hussien Abdullah
Format:	thesis
Language:	eng
Published:	2019
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spellingShingle	Al-Amoodi, Abdullah Hussien Abdullah Missing data imputation framework for early childhood longitudinal data: a study case on NCDRC data
description	This research aims to develop an imputation framework for the National ChildhoodDevelopment Research Centre (NCDRC)s missing data. Missing data and other associatedissues, such as outliers, time points, noise, and continuity, were the main challenges in thisresearch. The nature of the NCDRC dataset was not consistent with those reported in theliterature, with the latter being more randomly scattered and copious and having nopatterns, making it difficult to find and select relevant experimental data. The VIseKriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was utilized to select thebest continuous portion of Body Mass Index (BMI) data over 182 different portions, whichaccounted for 911 participants (i.e. children with complete records) over seven (7) continuoustime points. Three different machine learning algorithms to impute the missing data weretested and evaluated, namely K-nearest Neighbour (KNN), Nave Bayes (NB), and DecisionTree (DT). Three evaluation performance indicators, namely t-test, Coefficient of Determination,and Root Mean Square Error, were used in the experiment using three configurations based on 5%,10%, and 15% missing data. The results of the experiment showed that KNNs performance scores weresignificantly higher than those of the other algorithms. Out of all scores, KNN achieved 95.23% ofthe scores, followed by NB with 94.04% and DT with 83.33 %, clearly indicating that KNNoutperformed DT and NB in the imputation of missing data. In conclusion, the main findingsuggests that the KNN algorithm is the most effective algorithm for imputing missing data. Theimplication of this study is that practitioners, especially NCDRCs personnel, can use the proposedmissing data imputation framework to help impute missing data of similar datasets.
format	thesis
qualification_name
qualification_level	Doctorate
author	Al-Amoodi, Abdullah Hussien Abdullah
author_facet	Al-Amoodi, Abdullah Hussien Abdullah
author_sort	Al-Amoodi, Abdullah Hussien Abdullah
title	Missing data imputation framework for early childhood longitudinal data: a study case on NCDRC data
title_short	Missing data imputation framework for early childhood longitudinal data: a study case on NCDRC data
title_full	Missing data imputation framework for early childhood longitudinal data: a study case on NCDRC data
title_fullStr	Missing data imputation framework for early childhood longitudinal data: a study case on NCDRC data
title_full_unstemmed	Missing data imputation framework for early childhood longitudinal data: a study case on NCDRC data
title_sort	missing data imputation framework for early childhood longitudinal data: a study case on ncdrc data
granting_institution	Universiti Pendidikan Sultan Idris
granting_department	Fakulti Seni, Komputeran dan Industri Kreatif
publishDate	2019
url	https://ir.upsi.edu.my/detailsg.php?det=6762
_version_	1747833306800455680
spelling	oai:ir.upsi.edu.my:67622022-02-16 Missing data imputation framework for early childhood longitudinal data: a study case on NCDRC data 2019 Al-Amoodi, Abdullah Hussien Abdullah This research aims to develop an imputation framework for the National ChildhoodDevelopment Research Centre (NCDRC)s missing data. Missing data and other associatedissues, such as outliers, time points, noise, and continuity, were the main challenges in thisresearch. The nature of the NCDRC dataset was not consistent with those reported in theliterature, with the latter being more randomly scattered and copious and having nopatterns, making it difficult to find and select relevant experimental data. The VIseKriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was utilized to select thebest continuous portion of Body Mass Index (BMI) data over 182 different portions, whichaccounted for 911 participants (i.e. children with complete records) over seven (7) continuoustime points. Three different machine learning algorithms to impute the missing data weretested and evaluated, namely K-nearest Neighbour (KNN), Nave Bayes (NB), and DecisionTree (DT). Three evaluation performance indicators, namely t-test, Coefficient of Determination,and Root Mean Square Error, were used in the experiment using three configurations based on 5%,10%, and 15% missing data. The results of the experiment showed that KNNs performance scores weresignificantly higher than those of the other algorithms. Out of all scores, KNN achieved 95.23% ofthe scores, followed by NB with 94.04% and DT with 83.33 %, clearly indicating that KNNoutperformed DT and NB in the imputation of missing data. In conclusion, the main findingsuggests that the KNN algorithm is the most effective algorithm for imputing missing data. Theimplication of this study is that practitioners, especially NCDRCs personnel, can use the proposedmissing data imputation framework to help impute missing data of similar datasets. 2019 thesis https://ir.upsi.edu.my/detailsg.php?det=6762 https://ir.upsi.edu.my/detailsg.php?det=6762 text eng closedAccess Doctoral Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif Agyei, S. B., van der Weel, F. R., & van der Meer, A. L. (2016). Longitudinal study ofpreterm and full-term infants: high-density EEG analyses of cortical activity in response to visualmotion. Neuropsychologia, 84, 89-104.Ahangari, H., Atkinson-Palombo, C., & Garrick, N. W. (2016). Progress towards zero, aninternational comparison: Improvements in traffic fatality from 1990 to 2010 for different agegroups in the USA and 15 of its peers. Journal of safety research, 57, 61-70.Al Gharaibeh, F. M. (2015). 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