Predicting Unmet Healthcare Needs in Post-Disaster: A Machine Learning Approach
Hyun Jin Han1,2, Hae Sun Suh1,2,3
1Department of Regulatory Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea.
International Journal of Environmental Research and Public Health
|October 14, 2023
Summary
Disaster survivors often face unmet healthcare needs, impacting recovery. Machine learning models effectively predict these needs, identifying key factors like health status and location for better disaster management.
Area of Science:
- Public Health
- Health Services Research
- Disaster Medicine
Background:
- Unmet healthcare needs are a significant challenge post-disaster, worsening health disparities.
- Effective prediction and management of these needs are crucial for community recovery.
Purpose of the Study:
- To assess and predict unmet healthcare needs following disasters.
- To develop an accurate predictive model using machine learning.
- To identify key factors influencing unmet healthcare needs.
Main Methods:
- Analysis of data from the 2017 Long-term Survey on the Change of Life of Disaster Victims in South Korea.
- Application of machine learning algorithms: logistic regression, C5.0, and random forest.
- Feature selection based on Andersen's health behavior model and disaster-related factors.
Main Results:
- 31.5% of 1659 participants reported unmet healthcare needs.
- Random forest model demonstrated superior performance (precision, accuracy, AUC-ROC, F1-score).
- Key predictors included subjective health status, disaster-related conditions, and residential area.
Conclusions:
- Machine learning offers a valuable tool for post-disaster healthcare management.
- Identifying vulnerable populations and influencing factors is essential for policy development.
- Findings underscore the need for targeted interventions to address unmet healthcare needs in disaster-affected areas.
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