Application of Formal Concept Analysis and Data Mining to Characterize Infant Mortality in Two Regions of the State of Minas Gerais

Deivid Santos, Cristiane Nobre, Luis Zarate, Mark Song

2022

Abstract

Infant mortality is characterized by the death of children under one year, a problem that affects a large part of the world population. This article applies the Formal Concept Analysis (FCA), a mathematical technique used in data analysis to characterize infant mortality in two regions of Minas Gerais state - Brazil: Belo Horizonte and Vale do Jequitinhonha. The Metropolitan Region of Belo Horizonte has the best human development rate, and Vale do Jequitinhonha has the worst social equality. The relationships between attributes and victims are identified through association rules and implications.

Paper Citation

in Harvard Style

Santos D., Nobre C., Zarate L. and Song M. (2022). Application of Formal Concept Analysis and Data Mining to Characterize Infant Mortality in Two Regions of the State of Minas Gerais. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-569-2, pages 155-162. DOI: 10.5220/0011039900003179

in Bibtex Style

@conference{iceis22,
author={Deivid Santos and Cristiane Nobre and Luis Zarate and Mark Song},
title={Application of Formal Concept Analysis and Data Mining to Characterize Infant Mortality in Two Regions of the State of Minas Gerais},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2022},
pages={155-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011039900003179},
isbn={978-989-758-569-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Application of Formal Concept Analysis and Data Mining to Characterize Infant Mortality in Two Regions of the State of Minas Gerais
SN - 978-989-758-569-2
AU - Santos D.
AU - Nobre C.
AU - Zarate L.
AU - Song M.
PY - 2022
SP - 155
EP - 162
DO - 10.5220/0011039900003179