使用XGBoost和SHAP探索抑郁症和初步查模型的声学相关性
Kwang-Ho Seok1, Jaeeun Shin2, Sung-Man Bae3
1School of AI Convergence, Global Cyber University, Cheonan 31228, Republic of Korea.
Behavioral sciences (Basel, Switzerland)
|December 30, 2025
概括
语音分析的声学特征显示了跟踪抑郁症状严重性的潜力. 这项研究为在诊断严重抑郁症 (MDD) 中使用语音生物标志物提供了基础.
科学领域:
- 计算精神病学是一种计算精神病学.
- 语音分析 语音分析
- 机器学习在心理健康中的应用
背景情况:
- 重度抑郁症 (MDD) 的诊断依赖于主观的症状报告.
- 需要对MDD严重程度和诊断的客观生物标志物.
- 声源声学特征越来越多地被探索为潜在的客观标记.
研究的目的:
- 调查声音声学特征是否与抑郁症状严重程度相关.
- 评估这些特征的初步预测能力,以区分主要抑郁症 (MDD) 和健康对照 (HC).
- 建立一个可重复的分析工作流程,以语音为基础的心理健康评估.
主要方法:
- 使用了MODMA数据集,包括23个MDD和29个HC个体.
- 使用openSMILE软件提取了6553个声学特征.
- 雇员斯皮尔曼相关性,组差异分析,后勤回归,PCA + XGBoost和SHAP分析.
主要成果:
- 来自MFCC的光谱特征显示了与PHQ-9抑郁症得分的中度,系统的关联.
- 使用顶级MFCC特征的后勤回归模型实现了严重性分类 (PHQ-9 ≥10) 的0.78的交叉验证AUC.
- PCA + XGBoost模型为MDD-HC分类产生了0.60的测试AUC,MFCC特征被SHAP分析确定为关键驱动因素.
结论:
- 初步证据表明,声学语音特征可以反映抑郁症症状的严重程度.
- 语音分析显示出作为MDD评估的补充工具的潜力,尽管临床有效性需要更大的数据集.
- 该研究为基于语音的心理健康研究提供了可重现的工作流.
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