IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i18p2828-d1476617.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Taynara de Oliveira Castellões & Paloma Maria Silva Rocha Rizol & Luiz Fernando Costa Nascimento, 2024. "Association between Premature Birth and Air Pollutants Using Fuzzy and Adaptive Neuro-Fuzzy Inference System (ANFIS) Techniques," Mathematics, MDPI, vol. 12(18), pages 1-12, September.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2828-:d:1476617
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/18/2828/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/18/2828/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 577(7788), pages 89-94, January.
    2. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    3. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "Addendum: International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 586(7829), pages 19-19, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexander P. L. Martindale & Carrie D. Llewellyn & Richard O. Visser & Benjamin Ng & Victoria Ngai & Aditya U. Kale & Lavinia Ferrante Ruffano & Robert M. Golub & Gary S. Collins & David Moher & Melis, 2024. "Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Joachim Meyer, 2024. "Doing AI: Algorithmic decision support as a human activity," Papers 2402.14674, arXiv.org, revised Apr 2024.
    3. Babak Abedin & Christian Meske & Iris Junglas & Fethi Rabhi & Hamid R. Motahari-Nezhad, 2022. "Designing and Managing Human-AI Interactions," Information Systems Frontiers, Springer, vol. 24(3), pages 691-697, June.
    4. Armando Vargas-Palacios & Nisha Sharma & Gurdeep S. Sagoo, 2023. "Cost-effectiveness requirements for implementing artificial intelligence technology in the Women’s UK Breast Cancer Screening service," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Yuming Jiang & Zhicheng Zhang & Wei Wang & Weicai Huang & Chuanli Chen & Sujuan Xi & M. Usman Ahmad & Yulan Ren & Shengtian Sang & Jingjing Xie & Jen-Yeu Wang & Wenjun Xiong & Tuanjie Li & Zhen Han & , 2023. "Biology-guided deep learning predicts prognosis and cancer immunotherapy response," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Shu Jiang & Jiguo Cao & Bernard Rosner & Graham A. Colditz, 2023. "Supervised two‐dimensional functional principal component analysis with time‐to‐event outcomes and mammogram imaging data," Biometrics, The International Biometric Society, vol. 79(2), pages 1359-1369, June.
    7. Helen M. L. Frazer & Carlos A. Peña-Solorzano & Chun Fung Kwok & Michael S. Elliott & Yuanhong Chen & Chong Wang & Jocelyn F. Lippey & John L. Hopper & Peter Brotchie & Gustavo Carneiro & Davis J. McC, 2024. "Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Minkyu Shin & Jin Kim & Bas van Opheusden & Thomas L. Griffiths, 2023. "Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty," Papers 2303.07462, arXiv.org, revised Apr 2023.
    9. Juexiao Zhou & Haoyang Li & Xingyu Liao & Bin Zhang & Wenjia He & Zhongxiao Li & Longxi Zhou & Xin Gao, 2023. "A unified method to revoke the private data of patients in intelligent healthcare with audit to forget," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    10. Sebastian Schleidgen & Orsolya Friedrich & Selin Gerlek & Galia Assadi & Johanna Seifert, 2023. "The concept of “interaction” in debates on human–machine interaction," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    11. Qianwei Zhou & Margarita Zuley & Yuan Guo & Lu Yang & Bronwyn Nair & Adrienne Vargo & Suzanne Ghannam & Dooman Arefan & Shandong Wu, 2021. "A machine and human reader study on AI diagnosis model safety under attacks of adversarial images," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    12. Mélanie Roschewitz & Galvin Khara & Joe Yearsley & Nisha Sharma & Jonathan J. James & Éva Ambrózay & Adam Heroux & Peter Kecskemethy & Tobias Rijken & Ben Glocker, 2023. "Automatic correction of performance drift under acquisition shift in medical image classification," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    13. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org.
    14. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    15. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    16. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    17. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    18. Vinay Singh & Bhasker Choubey & Stephan Sauer, 2024. "Liquidity forecasting at corporate and subsidiary levels using machine learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(3), September.
    19. Lessmann, Stefan & Voß, Stefan, 2017. "Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy," International Journal of Forecasting, Elsevier, vol. 33(4), pages 864-877.
    20. Maria Tzitiridou-Chatzopoulou & Georgia Zournatzidou & Michael Kourakos, 2024. "Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland," IJERPH, MDPI, vol. 21(7), pages 1-13, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2828-:d:1476617. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.