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Predicting Drowning Mortality Risk Using Machine Learning Models; A Retrospective Cohort Study
Yousef Veisani1, Ali Sahebi1, Masoud Jobaneh2,3
1Non-Communicable Diseases Research Center, Ilam University of Medical Sciences, Ilam, Iran.
Archives of Academic Emergency Medicine
|July 29, 2026
Summary
Machine learning models were evaluated to predict drowning mortality risk. Logistic regression showed the highest predictive power, identifying high-risk coastal areas as crucial for targeted prevention strategies to reduce drowning incidents.
Area of Science:
- Public Health
- Data Science
- Coastal Safety
Background:
- Drowning remains a significant global public health issue, particularly in tourist-frequented coastal regions.
- Accurate prediction of drowning mortality risk is essential for effective public health interventions.
Purpose of the Study:
- To investigate the predictive capabilities of machine learning models for estimating drowning-related mortality risk.
- To compare the performance of logistic regression, decision tree, and naïve Bayes algorithms in predicting drowning fatalities.
Main Methods:
- A retrospective cohort study analyzed 600 drowning incident cases from 2018-2023.
- Data preprocessing included K-Nearest Neighbors (KNN) imputation and Synthetic Minority Over-sampling Technique (SMOTE) balancing.
- Logistic regression, decision tree, and naïve Bayes models were evaluated for predictive accuracy, sensitivity, and specificity.
Main Results:
- Logistic regression achieved the highest predictive power with 51.67% accuracy and 60.02% AUC.
- Drowning location was the most significant predictor, with high-risk areas showing a 33-fold increased mortality risk (p < 0.001).
- Age and gender were not statistically significant predictors of fatal drowning in this cohort.
Conclusions:
- Logistic regression is the most effective and interpretable model for assessing drowning mortality risk.
- Preventative strategies must prioritize identifying and managing high-risk coastal areas.
- Enhanced coastal safety measures, including signage, lifeguards, and public education, are vital to reduce drowning fatalities.
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