童年环境不稳定及其行为影响:一种机器学习方法来研究童年不良经历
Priscilla Mansah Codjoe1, Nii Adjetey Tawiah2, Daniel Alhassan3
1College of Arts and Sciences, Southern Illinois University Edwardsville, Campus Box 1653, Edwardsville, IL 62026, USA.
Behavioral sciences (Basel, Switzerland)
|June 26, 2024
概括
儿童不良经历 (ACE) 与长期健康问题有关. 家庭结构和频繁的搬迁是儿童ACE的关键预测因素,为目标干预提供信息.
科学领域:
- 儿童心理学 儿童心理学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 儿童不良经历 (ACE) 包括虐待,忽视和家庭功能障碍,显著影响长期健康和心理健康.
- ACE与慢性疾病,心理健康状况和社会挑战的风险增加有关.
研究的目的:
- 确定影响0-17岁儿童ACE的关键因素.
- 开发一种用于预测ACE概率的预测机器学习模型.
- 分析预测因子和ACE发生之间的相互作用.
主要方法:
- 机器学习模型被用来分析预测器和ACE之间的关系.
- 性能指标包括AUC,F1得分,回忆和精度评估的模型有效性.
- 神经网络被用于最终的预测模型.
主要成果:
- 单亲家庭和频繁的搬迁成为ACE的重要预测因素.
- 神经网络模型的AUC为0.788,精度为0.683,回忆率为0.707.
- 该模型在检测多个ACE的儿童方面表现出很高的准确性,但在单个ACE实例方面面临挑战.
结论:
- 家庭和住宅的稳定对于儿童的福祉至关重要.
- 调查结果对儿童福利政策和干预措施有重大影响.
- 针对性教育和医疗保健支持对处于危险的儿童至关重要.
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