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A Data Mining Approach to Identify Sexuality Patterns in a Brazilian University Population
Priscyla Waleska Simões1, Samuel Cesconetto1, Larissa Letieli Toniazzo de Abreu1
1Research Group in Information and Communications Technology in Health, UNESC, Criciúma, SC, Brazil.
Studies in Health Technology and Informatics
|August 12, 2015
Summary
This study used data mining to analyze sexuality and gender violence among university students in Brazil. Findings reveal distinct behavioral models and risk factors, aiding in understanding sexuality experiences.
Area of Science:
- Social Sciences
- Public Health
- Data Science
Background:
- University populations are key demographics for understanding societal trends.
- Gender violence and sexuality experiences are complex and require nuanced analysis.
- Data mining offers powerful tools for uncovering patterns in large datasets.
Purpose of the Study:
- To profile and understand the experience of sexuality using data mining.
- To identify relationships between variables in sexuality and gender violence data.
- To distinguish subgroups within a university population based on sexuality experiences.
Main Methods:
- Data mining classification task applied to a sexuality and gender violence database.
- Analysis of a university population in southern Brazil.
- Identification of key variable relationships to define behavioral models.
Main Results:
- Two significant relationships between variables were identified.
- Subgroups with distinct sexuality profiles and experiences were distinguished.
- Behavioral models and risk factors associated with sexuality were defined.
Conclusions:
- Data mining effectively profiles sexuality experiences and identifies risk factors.
- Understanding subgroups is crucial for targeted interventions regarding sexuality and gender violence.
- The identified models can inform the development of data mining algorithms for public health.
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