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Background <p>Poly-victimization (PV), encompassing multiple forms of victimization including physical abuse, emotional maltreatment, neglect, and peer violence, poses a significant public health challenge among children, particularly in rural areas with high rates of children whose parents have migrated to cities for work, leaving them in rural areas (left-behind children). This study investigates PV among rural children in the Chaoshan region of China, an area with distinct economic and cultural characteristics.</p> Methods <p>A thematic survey on PV occurrence was conducted among rural children in Shantou and Jieyang areas using a unified strategy. Four machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Random Forest (RF), were employed to predict PV risk, and SHAP feature importance was utilized to evaluate risk factors. An early-warning index for PV was constructed using linear regression and feature importance.</p> Results <p> <?tk 3?>Children in Jieyang were 1.84 times more likely to experience PV compared to those in Shantou (22.95% vs 12.49%). Among PV victims, left-behind children showed a notably higher proportion in Shantou (46.09%) compared to Jieyang (24.48%). The study successfully established specific predictive models for PV among rural children, with an overall prediction accuracy exceeding 80% across regions and 82% for left-behind children. The SHAP framework revealed significant risk factors, such as witnessing school bullying (contributing up to 22.72%) and self-harm intentions (up to 16.43%). The early-warning index demonstrated that the region and left-behind status significantly impacted PV occurrence. Specifically, the PV warning indices for Shantou and Jieyang were 0.621 (IQR: 0.558–0.761) and 0.497 (IQR: 0.422–0.658), respectively, significantly higher than the non-PV warning indices of 0.253 (IQR: 0.037–0.380) and 0.161 (IQR: 0.104–0.256). Left-behind children had higher PV warning indices than non-left-behind children.</p> Conclusions <p>This study demonstrates the utility of machine learning models in predicting PV among rural children, particularly left-behind children, in the Chaoshan region. Identifying risk factors and developing an early-warning index provide valuable tools for injury prevention, risk assessment, and targeted interventions, with potential applications in public health policy.</p>

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Employing machine learning for early detection of poly-victimization in rural children: a survey study in China’s Chaoshan region

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月5日

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  • Zicheng Cao,
  • Yandong Luo,
  • Ping Chang,
  • Jiajun Zhou,
  • Liping Li

摘要

Background

Poly-victimization (PV), encompassing multiple forms of victimization including physical abuse, emotional maltreatment, neglect, and peer violence, poses a significant public health challenge among children, particularly in rural areas with high rates of children whose parents have migrated to cities for work, leaving them in rural areas (left-behind children). This study investigates PV among rural children in the Chaoshan region of China, an area with distinct economic and cultural characteristics.

Methods

A thematic survey on PV occurrence was conducted among rural children in Shantou and Jieyang areas using a unified strategy. Four machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Random Forest (RF), were employed to predict PV risk, and SHAP feature importance was utilized to evaluate risk factors. An early-warning index for PV was constructed using linear regression and feature importance.

Results

Children in Jieyang were 1.84 times more likely to experience PV compared to those in Shantou (22.95% vs 12.49%). Among PV victims, left-behind children showed a notably higher proportion in Shantou (46.09%) compared to Jieyang (24.48%). The study successfully established specific predictive models for PV among rural children, with an overall prediction accuracy exceeding 80% across regions and 82% for left-behind children. The SHAP framework revealed significant risk factors, such as witnessing school bullying (contributing up to 22.72%) and self-harm intentions (up to 16.43%). The early-warning index demonstrated that the region and left-behind status significantly impacted PV occurrence. Specifically, the PV warning indices for Shantou and Jieyang were 0.621 (IQR: 0.558–0.761) and 0.497 (IQR: 0.422–0.658), respectively, significantly higher than the non-PV warning indices of 0.253 (IQR: 0.037–0.380) and 0.161 (IQR: 0.104–0.256). Left-behind children had higher PV warning indices than non-left-behind children.

Conclusions

This study demonstrates the utility of machine learning models in predicting PV among rural children, particularly left-behind children, in the Chaoshan region. Identifying risk factors and developing an early-warning index provide valuable tools for injury prevention, risk assessment, and targeted interventions, with potential applications in public health policy.