Association between Premature Birth and Air Pollutants Using Fuzzy and Adaptive Neuro-Fuzzy Inference System (ANFIS) Techniques

Taynara de Oliveira Castellões (), Paloma Maria Silva Rocha Rizol and Luiz Fernando Costa Nascimento
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Taynara de Oliveira Castellões: Faculdade de Engenharia e Ciências (FEG), Universidade Estadual Paulista (UNESP), Guaratinguetá 12516-410, Brazil
Paloma Maria Silva Rocha Rizol: Faculdade de Engenharia e Ciências (FEG), Universidade Estadual Paulista (UNESP), Guaratinguetá 12516-410, Brazil
Luiz Fernando Costa Nascimento: Faculdade de Engenharia e Ciências (FEG), Universidade Estadual Paulista (UNESP), Guaratinguetá 12516-410, Brazil

Mathematics, 2024, vol. 12, issue 18, 1-12

Abstract: This article uses machine learning techniques as fuzzy and neuro-fuzzy ANFISs, to develop and compare prediction models capable of relating pregnant women’s exposure to air pollutants, such as Nitrogen Dioxide and Particulate Matter, the mother’s age, and the number of prenatal consultations to the incidence of premature birth. In the current literature, studies can be found that relate prematurity to the exposure of pregnant women to NO 2 , O 3 , and PM 10 ; to Toluene and benzene, mainly in the window 5 to 10 days before birth; and to PM 10 in the week before birth. Both models used logistic regression to quantify the effects of pollutants as a result of premature birth. Datasets from Brazil—Departamento de Informatica do Sistema Único de Saúde (DATASUS) and Companhia Ambiental do Estado de São Paulo (CETESB)—were used, covering the period from 2016 to 2018 and comprising women living in the city of São José dos Campos (SP), Brazil. In order to evaluate and compare the different techniques used, evaluation metrics were calculated, such as correlation (r), coefficient of determination (R 2 ), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Error (MAE). These metrics are widely used in the literature due to their ability to evaluate the robustness and efficiency of prediction models. For the RMSE, MAPE, MSE, and MAE metrics, lower values indicate that prediction errors are smaller, demonstrating better model accuracy and confidence. In the case of (r) and R 2 , a positive and strong result indicates alignment and better performance between the real and predicted data. The neuro-fuzzy ANFIS model showed superior performance, with a correlation (r) of 0.59, R 2 = 0.35, RMSE = 2.83, MAPE = 5.35%, MSE = 8.00, and MAE = 1.70, while the fuzzy model returned results of r = 0.20, R 2 = 0.04, RMSE = 3.29, MSE = 10.81, MAPE = 6.67%, and MAE = 2.01. Therefore, the results from the ANFIS neuro-fuzzy system indicate greater prediction capacity and precision in relation to the fuzzy system. This superiority can be explained by integration with neural networks, allowing data learning and, consequently, more efficient modeling. In addition, the findings obtained in this study have potential for the formulation of public health policies aimed at reducing the number of premature births and promoting improvements in maternal and neonatal health.

Keywords: fuzzy systems; neuro-fuzzy approaches; prematurity; air pollutants; public health (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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