预测儿童和青少年孤独风险:一项机器学习研究
Jie Zhang1, Xinyi Feng1, Wenhe Wang1
1Research Center for Medicine and Social Development, School of Public Health, Chongqing Medical University, No. 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, China.
Behavioral sciences (Basel, Switzerland)
|October 26, 2024
概括
机器学习模型可以有效地预测童年孤独,识别关键的风险因素,如同行沟通和互联网成. 这种方法有助于对这种日益严重的公共卫生问题进行早期识别和干预.
科学领域:
- 儿童和青少年心理学 儿童和青少年心理学
- 计算精神病学是一种计算精神病学.
- 公共卫生信息学 公共卫生信息学
背景情况:
- 儿童和青少年的孤独是一个日益严重的公共卫生问题.
- 有效的干预措施需要预测模型和儿童孤独风险因素的识别.
研究的目的:
- 确定最佳的机器学习 (ML) 技术来预测学龄儿童的孤独感.
- 为了确定与孤独相关的关键风险因素,在这个人口群体.
主要方法:
- 未来的队列研究在重庆,中国,有822名参与者 (年龄11-16岁).
- 五个ML模型 (随机森林,XGBoost,物流回归,神经网络,SVM) 用于预测.
- 预测因素包括人口,家长,心理健康,行为和环境因素;28项最终确定.
主要成果:
- 所有的ML模型都显示出有利的预测准确性.
- 极端梯度提升 (XGBoost) 显示了最高的曲线下面积 (AUC) 为0.87.
- 孤独的关键预测因素包括同行沟通,一般影响,同行疏远和互联网成.
结论:
- 机器学习在预测和识别儿童和青少年孤独方面具有显著的潜力.
- 通过ML早期识别可以促进对儿童孤独的及时和有针对性的干预.
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