表达式:使用机器学习对出生意图的预测分析
1Kongju National University, Gongju.
概括
这项研究使用机器学习来预测出生意图,发现母亲的压力,关系满意度和伴侣冲突是关键因素. 了解这些预测因素可以帮助支持家庭.
科学领域:
- 生殖健康 生殖健康
- 医疗保健中的机器学习
- 家庭社会学 家庭社会学
背景情况:
- 出生意图是生殖决策的关键因素.
- 识别出产意向的预测因素对于有效的计划生育和支持至关重要.
- 之前的研究已经探讨了各种人口和心理社会因素,但预测建模仍然是一个活跃的领域.
研究的目的:
- 为了确定出生意图的关键决定因素.
- 开发和评估用于预测未来出生意图的机器学习模型.
- 为了对特定的出生意图有风险的个体进行分类.
主要方法:
- 使用韩国幼儿教育和护理小组 (K-ECEC-P) 分析了来自2580名受试者的数据.
- 机器学习技术的应用,包括决策树,随机森林分类器和物流回归.
- 使用精度,准确度,回忆,F1得分和曲线下的面积 (AUC) 评估模型性能.
主要成果:
- 随机森林模型实现了82%的AUC,证明了强大的预测性能.
- 确定的主要预测因素包括婚姻时期,年龄,压力,产前体重,孩子数量,母亲的抚养行为,伴侣冲突,婚姻满意度,抑郁症和出生类型.
- 母亲的心理状态 (压力,抑郁) 和关系因素 (冲突,满意度) 是重要的预测因素.
结论:
- 机器学习模型可以有效地预测出生的意图.
- 母亲的心理健康和关系动态是决定出生的意图的关键因素.
- 在产后期间,以家庭为中心的护理和解决这些因素的社会支持至关重要.
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