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Type :thesis
Subject :QA Mathematics
Main Author : Al-Amoodi, Abdullah Hussien Abdullah
Title :Missing data imputation framework for early childhood longitudinal data: a study case on NCDRC data
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2019
Notes :pakk
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Guest :Click to view PDF file

Abstract : Universiti Pendidikan Sultan Idris
This research aims to develop an imputation framework for the National Childhood Development  Research  Centre  (NCDRC)’s  missing  data.  Missing  data  and  other associated  issues, such as outliers, time points, noise, and continuity, were the main challenges in this  research. The nature of the NCDRC dataset was not consistent with those  reported  in  the   literature,  with  the  latter  being  more  randomly  scattered  and copious  and  having  no   patterns,  making  it  difficult  to  find  and  select  relevant experimental   data.   The    VIseKriterijumska   Optimizacija   Kompromisno   Resenje (VIKOR) method was utilized to select the  best continuous portion of Body Mass Index (BMI)  data  over  182  different  portions,  which   accounted  for  911  participants  (i.e. children with complete records) over seven (7) continuous  time points. Three different machine  learning  algorithms  to  impute  the  missing  data  were   tested  and  evaluated, namely  K-nearest  Neighbour  (KNN),  Naïve  Bayes  (NB),  and  Decision   Tree  (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 KNN’s performance scores were  significantly higher than those of the other algorithms. Out of all scores, KNN achieved 95.23% of  the scores, followed by NB with 94.04% and DT with 83.33 %, clearly indicating that KNN  outperformed DT and NB in the imputation  of  missing data.  In  conclusion,  the  main  finding   suggests  that  the  KNN algorithm is the most effective algorithm for imputing missing data. The  implication of this study is that practitioners, especially NCDRC’s personnel, can use the proposed  missing data imputation framework to help impute missing data of similar datasets.

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