机器学习算法的构建和验证,用于在大规模的COVID-19爆发期间预测家庭隔离人员的抑郁症:基于Adaboost模型
Yiwei Zhou1,2,3, Zejie Zhang4, Qin Li5
1Business School, University of Shanghai for Science and Technology, 200093, Shanghai, China.
BMC psychology
|April 24, 2024
概括
这项研究开发了一种Adaboost机器学习模型,用于预测COVID-19期间在家隔离人员的抑郁症. 该模型在识别抑郁症水平方面表现出高准确性和有效性.
科学领域:
- 机器学习 机器学习
- 公共卫生 公共卫生
- 流行病学 流行病学
背景情况:
- COVID-19 流行病与增加的抑郁率有关.
- 准确识别家庭隔离人口的抑郁症至关重要.
- 现有的方法可能无法充分捕捉与流行病相关的心理健康挑战的细微差别.
研究的目的:
- 为在COVID-19疫情期间在家中隔离的个人构建和验证基于机器学习的抑郁症预测模型.
- 为了比较用于抑郁症预测的多个机器学习算法的性能.
- 确定一个强大的和可通用的模型来识别抑郁风险.
主要方法:
- 一项横截面研究调查了在家隔离的个人,采集了社会人口统计数据,COVID-19的影响和使用PHQ-9尺度的抑郁症.
- 一组数据被随机分为培训 (70%) 和验证 (30%) 组.
- 通过十倍交叉验证评估了15个机器学习模型,通过准确性,精度,ROC曲线和AUC来评估性能.
主要成果:
- 在家庭隔离人员中,抑郁症的患病率为31.66%.
- 该模型的准确度为0.7180和AUC为0.7803,超过了其他15种模型.
- 在验证组中,Adaboost表现强,AUC超过0.83.
结论:
- 在疫情期间,Adaboost机器学习算法有效地预测了家庭隔离人员的抑郁症.
- 开发的模型表现出卓越的机器学习性能,有效性,稳定性和通用性.
- 该模型为在公共卫生危机期间的心理健康监测和干预提供了有价值的工具.
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