[使用随机森林模型影响青少年性经验的因素:2019~2021年韩国青少年风险行为网络调查数据的二次数据分析]
Yoonseok Yang1, Ju Won Kwon2, Youngran Yang3
1Research Center of Healthcare & Welfare Instrument for the Aged, Division of Biomedical Engineering, College of Engineering, Jeonbuk National University, Jeonju, Korea.
Journal of Korean Academy of Nursing
|June 12, 2024
概括
通过随机森林分析,可以预测青少年的性经验. 关键因素包括久坐时间,互联网使用和物质开始,指导有针对性的健康干预.
科学领域:
- 青少年健康 青少年健康
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 青少年的性经验受到各种行为和环境因素的影响.
- 了解这些影响对于开发有效的健康干预措施至关重要.
研究的目的:
- 使用随机森林方法开发青少年性经验的预测模型.
- 确定影响这些体验的关键变量.
主要方法:
- 利用了2019-2021年韩国青年风险行为网络调查 (N=167,199) 的数据.
- 采用随机森林算法进行预测建模.
- 使用SPSS和Python进行统计分析,包括Rao-Scott χ2和复杂的样本t测试.
主要成果:
- 确定了性经验的主要预测因素:久坐时间,互联网使用,购买香烟的便利性,首次饮酒的年龄,吸烟的开始,早餐的消费以及购买酒精的困难.
- 在COVID-19大流行期间观察到性经验流行率的波动趋势.
结论:
- 将健康行为干预 (互联网,吸烟,酒精) 与青少年性健康计划相结合.
- 在疾病预防和护理方面利用随机森林分析进行高性能预测建模.
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