UPSI Digital Repository (UDRep)
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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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