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Non-Fatal Drowning Risk Prediction Based on Stacking Ensemble Algorithm.

Xinshan Xie1,2, Zhixing Li2,3, Haofeng Xu4

  • 1School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510200, China.

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|September 23, 2022
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Summary
This summary is machine-generated.

Machine learning models can predict non-fatal drowning risk in children. A stacking ensemble model demonstrated superior performance, identifying key risk factors like swimming habits and personality traits.

Keywords:
drowningmachine learningpredictionrisk-factorsstacking ensemble

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Area of Science:

  • Public Health
  • Data Science
  • Pediatrics

Background:

  • Drowning is a significant public health issue and a leading cause of child mortality in developing nations.
  • Accurate prediction of non-fatal drowning incidents is crucial for targeted prevention strategies.

Purpose of the Study:

  • To develop and compare machine learning (ML) algorithms for predicting non-fatal drowning risk in children.
  • To identify key risk factors associated with non-fatal drowning events.

Main Methods:

  • Data from 8390 children in Qingyuan city, China, were analyzed.
  • Four ML models were developed: logistic regression (LR), random forest (RF), support vector machine (SVM), and a stacking ensemble model.
  • Model performance was evaluated using AUC, F1 score, accuracy, sensitivity, and specificity.

Main Results:

  • 12.07% of children experienced non-fatal drowning.
  • Identified risk factors include swimming frequency in open water, proximity to open water, swimming skills, introverted personality, and family relationships.
  • The stacking ensemble model achieved the highest performance (AUC = 0.741).

Conclusions:

  • Stacking ensemble algorithms show promise for outperforming other ML models in non-fatal drowning prediction.
  • Identifying specific risk factors can inform public health interventions to reduce drowning incidents in children.