Analysis of Simple Data Imputation in Disease Dataset

DOI

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/icst-18.2018.98
ID  - Anggraeny2018/12
ER  -