Proceedings of the International Conference on Science and Technology (ICST 2018)

Analysis of Simple Data Imputation in Disease Dataset

Authors
Fetty Tri Anggraeny, Intan Yuniar Purbasari, M. Syahrul Munir, Faisal Muttaqin, Eka Prakarsa Mandyarta, Fawwaz Ali Akbar
Corresponding Author
Fetty Tri Anggraeny
Available Online December 2018.
DOI
https://doi.org/10.2991/icst-18.2018.98How to use a DOI?
Keywords
analysis; simple Imputation; disease dataset; fuzzy c-means
Abstract
In the statistical data collection it is very possible that there are variables that do not respond or in other words empty, called missing value, that can cause problems in data analysis. In this research we will analyze some simple imputation technique to solve the missing value problem, are zero imputation, mean imputation median imputation, and random imputation. This study used a Pima Indians, hepatitis and breast cancer Wisconsin dataset from UCI Machine Learning. We also compare with incomplete data removal technique. The application of various simple imputations in the disease dataset can increase the accuracy value when compared to deficient data deletion techniques. And the zero imputation technique shows the best performance compared to other imputation techniques and deficient data removal techniques.
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Proceedings
International Conference on Science and Technology (ICST 2018)
Part of series
Atlantis Highlights in Engineering
Publication Date
December 2018
ISBN
978-94-6252-650-1
ISSN
2589-4943
DOI
https://doi.org/10.2991/icst-18.2018.98How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Fetty Tri Anggraeny
AU  - Intan Yuniar Purbasari
AU  - M. Syahrul Munir
AU  - Faisal Muttaqin
AU  - Eka Prakarsa Mandyarta
AU  - Fawwaz Ali Akbar
PY  - 2018/12
DA  - 2018/12
TI  - Analysis of Simple Data Imputation in Disease Dataset
BT  - International Conference on Science and Technology (ICST 2018)
PB  - Atlantis Press
SP  - 471
EP  - 475
SN  - 2589-4943
UR  - https://doi.org/10.2991/icst-18.2018.98
DO  - https://doi.org/10.2991/icst-18.2018.98
ID  - Anggraeny2018/12
ER  -