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Applying Formal Concept Analysis for the Recognition of Infant Mortality Patterns
Lorenzo Marzano1, Cristiane Nobre1, Luis Zárate1
1Department of Computer Science, Pontifical Catholic University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Studies in Health Technology and Informatics
|June 8, 2022
Summary
Infant mortality (IM) patterns were analyzed in Brazil's Amapá region using Formal Concept Analysis. This research identifies key factors contributing to severe infant mortality rates, informing targeted public health interventions.
Area of Science:
- Public Health
- Social Epidemiology
- Data Science
Background:
- Infant mortality (IM) is a critical social indicator reflecting socioeconomic development and maternal-child healthcare access.
- High IM rates are linked to biological, behavioral, and public health challenges.
- The Brazilian Amazon, specifically Amapá, faces severe infant mortality challenges.
Purpose of the Study:
- To identify patterns and associations related to infant mortality in Amapá, Brazil.
- To leverage data analysis for understanding the drivers of severe IM.
- To inform the development of targeted public health policies.
Main Methods:
- Formal Concept Analysis (FCA) was employed to analyze birth data.
- Implication and association rules were derived from the data.
- Pattern recognition was applied to identify significant factors in IM.
Main Results:
- Specific patterns associated with infant mortality in Amapá were identified.
- Key contributing factors to severe IM were recognized through data analysis.
- The study provides insights into the complex factors influencing infant mortality in the region.
Conclusions:
- Formal Concept Analysis is effective in uncovering patterns in infant mortality data.
- Understanding these patterns is crucial for developing effective public health strategies.
- The findings can guide policy interventions to reduce infant mortality in Amapá and similar regions.
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