IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2022i1p136-d1011392.html
   My bibliography  Save this article

Evaluating Time Influence over Performance of Machine-Learning-Based Diagnosis: A Case Study of COVID-19 Pandemic in Brazil

Author

Listed:
  • Julliana Gonçalves Marques

    (Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil)

  • Luiz Affonso Guedes

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil)

  • Márjory Cristiany da Costa Abreu

    (Department of Computing, Sheffield Hallam University, Sheffield S9 3TY, UK)

Abstract

Efficiently recognising severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) symptoms enables a quick and accurate diagnosis to be made, and helps in mitigating the spread of the coronavirus disease 2019. However, the emergence of new variants has caused constant changes in the symptoms associate with COVID-19. These constant changes directly impact the performance of machine-learning-based diagnose. In this context, considering the impact of these changes in symptoms over time is necessary for accurate diagnoses. Thus, in this study, we propose a machine-learning-based approach for diagnosing COVID-19 that considers the importance of time in model predictions. Our approach analyses the performance of XGBoost using two different time-based strategies for model training: month-to-month and accumulated strategies. The model was evaluated using known metrics: accuracy, precision, and recall. Furthermore, to explain the impact of feature changes on model prediction, feature importance was measured using the SHAP technique, an XAI technique. We obtained very interesting results: considering time when creating a COVID-19 diagnostic prediction model is advantageous.

Suggested Citation

  • Julliana Gonçalves Marques & Luiz Affonso Guedes & Márjory Cristiany da Costa Abreu, 2022. "Evaluating Time Influence over Performance of Machine-Learning-Based Diagnosis: A Case Study of COVID-19 Pandemic in Brazil," IJERPH, MDPI, vol. 20(1), pages 1-14, December.
    • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:136-:d:1011392
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/1/136/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/1/136/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mukund Sundararajan & Amir Najmi, 2019. "The many Shapley values for model explanation," Papers 1908.08474, arXiv.org, revised Feb 2020.
    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. Alireza Rezazadeh & Yasamin Jafarian & Ali Kord, 2022. "Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features," Forecasting, MDPI, vol. 4(1), pages 1-13, February.
    2. Hu'e Sullivan & Hurlin Christophe & P'erignon Christophe & Saurin S'ebastien, 2022. "Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring," Papers 2212.05866, arXiv.org, revised Jan 2025.
    3. Hugh Chen & Scott M. Lundberg & Su-In Lee, 2022. "Explaining a series of models by propagating Shapley values," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    4. Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2021. "Cohort Shapley value for algorithmic fairness," Papers 2105.07168, arXiv.org.
    5. Wenguang Zhang & Ting Lei & Yu Gong & Jun Zhang & Yirong Wu, 2022. "Using Explainable Artificial Intelligence to Identify Key Characteristics of Deep Poverty for Each Household," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    6. Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2022. "Variable importance without impossible data," Papers 2205.15750, arXiv.org, revised Apr 2023.
    7. Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).
    8. Ronald Richman & Mario V. Wuthrich, 2021. "LocalGLMnet: interpretable deep learning for tabular data," Papers 2107.11059, arXiv.org.
    9. Kumar, Rishabh & Koshiyama, Adriano & da Costa, Kleyton & Kingsman, Nigel & Tewarrie, Marvin & Kazim, Emre & Roy, Arunita & Treleaven, Philip & Lovell, Zac, 2023. "Deep learning model fragility and implications for financial stability and regulation," Bank of England working papers 1038, Bank of England.
    10. Joohyun Jang & Woonyoung Jeong & Sangmin Kim & Byeongcheon Lee & Miyoung Lee & Jihoon Moon, 2023. "RAID: Robust and Interpretable Daily Peak Load Forecasting via Multiple Deep Neural Networks and Shapley Values," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    11. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
    12. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2023. "Explainable FinTech lending," Journal of Economics and Business, Elsevier, vol. 125.
    13. Chen, Yujia & Calabrese, Raffaella & Martin-Barragan, Belen, 2024. "Interpretable machine learning for imbalanced credit scoring datasets," European Journal of Operational Research, Elsevier, vol. 312(1), pages 357-372.
    14. Disha Bhattacharyya & Sudeep Pradhan & Shabbiruddin, 2023. "Barriers in Replacement of Conventional Vehicles by Electric Vehicles in India: A Decision-Making Approach," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 15(1), pages 1-20, January.
    15. Farshad Khavari & Jay Liu, 2024. "A Hydrogen-Integrated Aggregator Model for Managing the Point of Common Coupling Congestion in Green Multi-Microgrids," Energies, MDPI, vol. 17(16), pages 1-20, August.
    16. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
    17. Lee Changro, 2022. "Training and Interpreting Machine Learning Models: Application in Property Tax Assessment," Real Estate Management and Valuation, Sciendo, vol. 30(1), pages 13-22, March.
    18. Wei Li & Denis Mike Becker, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Papers 2101.05249, arXiv.org, revised Jul 2021.
    19. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
    20. Mehmet Güney Celbiş & Pui‐hang Wong & Karima Kourtit & Peter Nijkamp, 2023. "Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(3), pages 559-584, April.

    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:jijerp:v:20:y:2022:i:1:p:136-:d:1011392. 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.