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Predicting Unmet Healthcare Needs in Post-Disaster: A Machine Learning Approach

Author

Listed:
  • Hyun Jin Han

    (Department of Regulatory Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
    Institute of Regulatory Innovation through Science (IRIS), Kyung Hee University, Seoul 02447, Republic of Korea)

  • Hae Sun Suh

    (Department of Regulatory Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
    Institute of Regulatory Innovation through Science (IRIS), Kyung Hee University, Seoul 02447, Republic of Korea
    College of Pharmacy, Kyung Hee University, Seoul 02447, Republic of Korea)

Abstract

Unmet healthcare needs in the aftermath of disasters can significantly impede recovery efforts and exacerbate health disparities among the affected communities. This study aims to assess and predict such needs, develop an accurate predictive model, and identify the key influencing factors. Data from the 2017 Long-term Survey on the Change of Life of Disaster Victims in South Korea were analyzed using machine learning techniques, including logistic regression, C5.0 tree-based model, and random forest. The features were selected based on Andersen’s health behavior model and disaster-related factors. Among 1659 participants, 31.5% experienced unmet healthcare needs after a disaster. The random forest algorithm exhibited the best performance in terms of precision, accuracy, Under the Receiver Operating Characteristic (AUC-ROC), and F-1 scores. Subjective health status, disaster-related diseases or injuries, and residential area have emerged as crucial factors predicting unmet healthcare needs. These findings emphasize the vulnerability of disaster-affected populations and highlight the value of machine learning in post-disaster management policies for decision-making.

Suggested Citation

  • Hyun Jin Han & Hae Sun Suh, 2023. "Predicting Unmet Healthcare Needs in Post-Disaster: A Machine Learning Approach," IJERPH, MDPI, vol. 20(19), pages 1-13, September.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:19:p:6817-:d:1246487
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    References listed on IDEAS

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    1. Sovacool, Benjamin K. & Tan-Mullins, May & Abrahamse, Wokje, 2018. "Bloated bodies and broken bricks: Power, ecology, and inequality in the political economy of natural disaster recovery," World Development, Elsevier, vol. 110(C), pages 243-255.
    2. Eberhardt, M.S. & Pamuk, E.R., 2004. "The importance of place of residence: Examining health in rural and nonrural areas," American Journal of Public Health, American Public Health Association, vol. 94(10), pages 1682-1686.
    3. Sheikh Kamran Abid & Noralfishah Sulaiman & Shiau Wei Chan & Umber Nazir & Muhammad Abid & Heesup Han & Antonio Ariza-Montes & Alejandro Vega-Muñoz, 2021. "Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    4. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    5. Suzanne Phibbs & Christine Kenney & Christina Severinsen & Jon Mitchell & Roger Hughes, 2016. "Synergising Public Health Concepts with the Sendai Framework for Disaster Risk Reduction: A Conceptual Glossary," IJERPH, MDPI, vol. 13(12), pages 1-21, December.
    6. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    7. Min Li & Hui Hou & Jufang Yu & Hao Geng & Ling Zhu & Yong Huang & Xianqiang Li, 2021. "Prediction of Power Outage Quantity of Distribution Network Users under Typhoon Disaster Based on Random Forest and Important Variables," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, January.
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