使用结构性和非结构性双重语言的机器学习来检测抑郁症.
Filza Rehmani1, Qaisar Shaheen1, Muhammad Anwar2
1Department of Computer Science & Information Technology The Islamia University of Bahawalpur Bannu Pakistan.
Healthcare technology letters
|August 5, 2024
概括
这项研究开发了罗马乌尔都语和英语数据集,以预测抑郁风险. 支持矢量机 (SVM) 模型表现最好,改善了南亚地区的抑郁症预测.
科学领域:
- 计算语言学计算语言学
- 心理健康信息学心理健康信息学
- 社交媒体分析
背景情况:
- 抑郁症显著影响心理健康和日常功能.
- 在全球范围内,社交媒体的使用正在增加,用户用巴基斯坦和印度的罗马乌尔都语等区域语言来表达自己.
- 以前的研究没有有效地利用罗马乌尔都语来预测抑郁症,尽管它很普遍.
研究的目的:
- 创建一个新的罗马乌尔都语数据集,用于抑郁风险预测.
- 通过使用双语数据 (罗马乌尔都语和英语) 评估机器学习模型在识别抑郁症方面的有效性.
- 通过语言特定分析,增强亚洲人群中抑郁症检测.
主要方法:
- 合并的数据集包括从Facebook手动转换的罗马乌尔都语评论和Kaggle的英语评论.
- 使用的机器学习模型:支持矢量机 (SVM),支持矢量机辐射基函数 (SVM-RBF),随机森林 (RF) 和来自变压器的双向编码器表示 (BERT).
- 将抑郁风险分为三个类别:非抑郁,中度和严重.
主要成果:
- 支持矢量机 (SVM) 模型在预测抑郁风险方面表现出0.84%的最高准确度.
- SVM的性能优于其他测试模型,包括SVM-RBF,RF和BERT.
- 这项研究强调了罗马乌尔都语在与英语结合时对抑郁症预测的重要贡献.
结论:
- 开发的罗马乌尔都语数据集和机器学习方法显示了改善亚洲国家抑郁风险评估的前景.
- SVM是使用混合语言社交媒体数据对抑郁症严重程度进行分类的有效模型.
- 这项研究有助于在数字环境中对心理健康有更细致的理解,特别是在非英语人口中.
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