使用Hwa-byung人格尺度评估Hwa-byung脆弱性:对机器学习方法的比较研究
Chan-Young Kwon1, Boram Lee2, Sung-Hee Kim3
1Department of Oriental Neuropsychiatry, College of Korean Medicine, Dong-Eui University, Busan, Republic of Korea.
Journal of pharmacopuncture
|January 1, 2025
概括
机器学习模型可以使用人格特征识别易受Hwa-byung (HB) 影响的个体. 一个整体模型显示出强大的预测性能,有助于早期临床评估和干预.
科学领域:
- 精神病学和行为科学
- 计算心理学 计算心理学
- 医疗信息学 医疗信息学
背景情况:
- 华 (HB) 是一种文化结合综合症,其特点是痛苦和体质症状.
- 准确识别易受HB感染的个体对于及时干预至关重要.
- 现有的评估方法可以从增强的预测能力中受益.
研究的目的:
- 开发和比较机器学习模型来分类HB漏洞.
- 用HB人格尺度来评估这些模型的预测效果.
- 为了确定HB症状严重程度的关键人格预测因素.
主要方法:
- 来自500名韩国成年人 (19-44岁) 的数据使用HB个性和症状尺度进行了分析.
- 使用了机器学习模型 (随机森林分类器,XGBoost,物流回归和集体).
- 用于特征选择和模型评估,使用递归特征消除与交叉验证.
主要成果:
- 16项HB人格尺度作为预测HB脆弱性的最佳特征.
- 一个整体模型 (RFC-XGC-LR) 在测试套件上实现了0.80的精度和0.86的AUROC.
- 第16项 ("我经常容易感到内") 是所有模型中最重要的预测因素.
结论:
- 机器学习模型,特别是集体方法,显示出对基于人格特征选具有Hwa-byung风险的个体有前途.
- 这些模型可以在临床环境中提高HB风险评估的效率和准确性.
- 通过这些数据驱动的方法,可以促进对HB的早期识别和干预.
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