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Missing Data Imputation in Cardiometabolic Risk Assessment: A Solution Based on Artificial Neural Networks
Computer Science and Information Systems, Tome 17 (2020) no. 2 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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A common problem when working with medical records is that some measurements are missing. The simplest and the most common solution, especially in machine learning domain, is to exclude records with incomplete data. This approach produces datasets with reduced statistical power and can even lead to biased or erroneous final results. There are, however, many proposed imputing methods for missing data. Although some of them, such as multiple imputation, are mature and well researched, they can be prone to misuse and are not always suitable for building complex frameworks. This paper explores neural networks as a potential tool for imputing univariate missing laboratory data during cardiometabolic risk assessment, comparing it to other simple methods that could be easily set up and used further in building predictive models. We have found that neural networks outperform other algorithms for diverse fraction of missing data and different mechanisms causing their missingness.
Keywords: missing data, cardiometabolic risk, artficial neural networks 
@article{CSIS_2020_17_2_a2,
     author = {Dunja Vrba\v{s}ki and Aleksandar Kupusinac and Rade Doroslova\v{c}ki and Edita Stoki\'c and Dragan Iveti\'c},
     title = {Missing {Data} {Imputation} in {Cardiometabolic} {Risk} {Assessment:} {A} {Solution} {Based} on {Artificial} {Neural} {Networks}},
     journal = {Computer Science and Information Systems},
     year = {2020},
     volume = {17},
     number = {2},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2020_17_2_a2/}
}
TY  - JOUR
AU  - Dunja Vrbaški
AU  - Aleksandar Kupusinac
AU  - Rade Doroslovački
AU  - Edita Stokić
AU  - Dragan Ivetić
TI  - Missing Data Imputation in Cardiometabolic Risk Assessment: A Solution Based on Artificial Neural Networks
JO  - Computer Science and Information Systems
PY  - 2020
VL  - 17
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/CSIS_2020_17_2_a2/
ID  - CSIS_2020_17_2_a2
ER  - 
%0 Journal Article
%A Dunja Vrbaški
%A Aleksandar Kupusinac
%A Rade Doroslovački
%A Edita Stokić
%A Dragan Ivetić
%T Missing Data Imputation in Cardiometabolic Risk Assessment: A Solution Based on Artificial Neural Networks
%J Computer Science and Information Systems
%D 2020
%V 17
%N 2
%U http://geodesic.mathdoc.fr/item/CSIS_2020_17_2_a2/
%F CSIS_2020_17_2_a2
Dunja Vrbaški; Aleksandar Kupusinac; Rade Doroslovački; Edita Stokić; Dragan Ivetić. Missing Data Imputation in Cardiometabolic Risk Assessment: A Solution Based on Artificial Neural Networks. Computer Science and Information Systems, Tome 17 (2020) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2020_17_2_a2/