Potential Confounders in the Analysis of Brazilian Adolescent’s Health: A Combination of Machine Learning and Graph Theory

Amanda Yumi Ambriola Oku, Guilherme Augusto Zimeo Morais, Ana Paula Arantes Bueno, André Fujita and João Ricardo Sato
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Amanda Yumi Ambriola Oku: Center of Mathematics, Computing and Cognition—Universidade Federal do ABC, Santo André CEP 09210-580, Brazil
Guilherme Augusto Zimeo Morais: Big Data—Hospital Israelita Albert Einstein, São Paulo CEP 05652-900, Brazil
Ana Paula Arantes Bueno: Center of Mathematics, Computing and Cognition—Universidade Federal do ABC, Santo André CEP 09210-580, Brazil
André Fujita: Institute of Mathematics and Statistics—University of São Paulo, São Paulo CEP 05508-090, Brazil
João Ricardo Sato: Center of Mathematics, Computing and Cognition—Universidade Federal do ABC, Santo André CEP 09210-580, Brazil

IJERPH, 2019, vol. 17, issue 1, 1-10

Abstract: The prevalence of health problems during childhood and adolescence is high in developing countries such as Brazil. Social inequality, violence, and malnutrition have strong impact on youth health. To better understand these issues we propose to combine machine-learning methods and graph analysis to build predictive networks applied to the Brazilian National Student Health Survey (PenSE 2015) data, a large dataset that consists of questionnaires filled by the students. By using a combination of gradient boosting machines and centrality hub metric, it was possible to identify potential confounders to be considered when conducting association analyses among variables. The variables were ranked according to their hub centrality to predict the other variables from a directed weighted-graph perspective. The top five ranked confounder variables were “gender”, “oral health care”, “intended education level”, and two variables associated with nutrition habits—“eat while watching TV” and “never eat fast-food”. In conclusion, although causal effects cannot be inferred from the data, we believe that the proposed approach might be a useful tool to obtain novel insights on the association between variables and to identify general factors related to health conditions.

Keywords: adolescent; machine-learning; network; graph; public health (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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