利用机器学习模型的数据增强来预测抑郁和焦虑,使用修订的儿童焦虑和抑郁量表临床报告
Saleha Noor1, Zamir Hussain1, Qurrat Ulain Hamdan2
1School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences & Technology (NUST), Islamabad, Pakistan.
Frontiers in psychiatry
|December 15, 2025
概括
机器学习,特别是随机森林算法,可以有效地选巴基斯坦的抑郁和焦虑. 这种方法提高了当前医疗保健资源的利用率,以对年轻人进行心理健康评估.
科学领域:
- 精神病学是一个精神病学.
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 巴基斯坦有1500万人受到抑郁和焦虑的影响.
- 有限的政府精神卫生机构和精神病医生需要有效的查工具.
- 迫切需要有效的干预策略来应对心理健康危机.
研究的目的:
- 利用机器学习 (ML) 与修订的儿童焦虑和抑郁量表 (RCADS) 进行高效的查.
- 为了最大限度地利用现有的医疗保健资源进行心理健康评估.
- 促进儿童和青少年对抑郁和焦虑的早期发现和干预.
主要方法:
- 使用了89个案例 (从最初的138个) 的数据集,其中包括47个RCADS项目.
- 特征选择涉及千平方,斯皮尔曼相关性和随机森林-递归特征消除.
- 进行了数据增强和训练六个ML算法 (决策树,随机森林,SVM,后勤回归,天真贝叶斯,KNN),在增强和外部数据集上验证了顶级模型.
主要成果:
- 随机森林算法实现了高达81%的宏观平均准确性,精度,回忆和F1分数.
- 发现最优的数据增强是原始数据集大小的四倍.
- 在RCADS项目05中,与研究群体中抑郁和焦虑评估的相关性较弱.
结论:
- 随机森林算法使用46个特征,是选焦虑和抑郁症的合适工具.
- 这种ML方法显示出作为心理健康专业人员的决策支持系统的潜力.
- 这项研究强调了一种有希望的方法,可以改善儿童焦虑和抑郁症的传统查.
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