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

Author(s)

Ambriola Oku, Amanda Yumi ^[1] ; Zimeo Morais, Guilherme Augusto ^[2] ; Arantes Bueno, Ana Paula ^[1] ; Fujita, Andre ^[3] ; Sato, Joao Ricardo ^[1]

Affiliation

^[1] Univ Fed ABC, Ctr Math Comp & Cognit, BR-09210580 Santo Andre, SP - Brazil

^[2] Big Data Hosp Israelita Albert Einstein, BR-05652900 Sao Paulo - Brazil

^[3] Univ Sao Paulo, Inst Math & Stat, BR-05508090 Sao Paulo - Brazil

• Document type: Journal article
• Source: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH; v. 17, n. 1 JAN 2020.
• Web of Science Citations: 0

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. (AU)

• FAPESP's process: 19/17907-5 - Inferences on interbrain networks using functional near-infrared spectroscopy: investigating child-adult interactions
• Grantee: Amanda Yumi Ambriola Oku
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• FAPESP's process: 18/21934-5 - Network statistics: theory, methods, and applications
• Grantee: André Fujita
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 16/19376-9 - Structural and functional Papez circuit integrity and its relations to symptomology in Amyotrophic Lateral Sclerosis
• Grantee: Ana Paula Arantes de Andrade Bueno
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 18/04654-9 - Time series, wavelets and high dimensional data
• Grantee: Pedro Alberto Morettin
• Support Opportunities: Research Projects - Thematic Grants