通过统计分析,机器学习和语义本体学来探索在线公共调查生活方式数据集
Ayan Chatterjee1,2,3, Michael A Riegler4, Miriam Sinkerud Johnson5
1Department of Information and Communication Technologies, University of Agder, 4879, Grimstad, Norway. ayan@nilu.no.
Scientific reports
|October 15, 2024
概括
生活方式和人口因素显著影响抑郁症症状,特别是在COVID-19大流行期间. 这项研究使用机器学习和在线调查来确定印度心理健康的关键风险因素.
科学领域:
- 公共卫生 公共卫生
- 心理健康研究 心理健康研究
- 数据科学数据科学数据科学
背景情况:
- 生活方式疾病是全球主要的健康问题,生活方式因素严重影响抑郁症.
- COVID-19大流行加剧了抑郁症的决定因素,增加了对促成因素的研究需求.
研究的目的:
- 在COVID-19大流行期间,确定与印度抑郁症症状相关的生活方式和人口因素.
- 开发和展示用于心理健康研究的在线调查数据收集和分析的方法.
主要方法:
- 一项匿名在线调查收集了印度加尔各答1767名参与者的数据.
- 使用了统计方法,包括主要组件分析 (PCA),方差分析 (ANOVA) 和机器学习算法,如支持向量机 (SVM) 和k-means集群.
- 局部可解释的模型不可知解释 (LIME) 和OWL本体学用于模型解释和数据表示.
主要成果:
- 一种支持矢量机 (SVM) 模型在分类与抑郁相关的因素方面取得了96%的准确性.
- 该研究发现生活方式,人口统计学变量和抑郁症状之间存在显著的相关性.
- 在线调查方法证明了数据收集,可视化和成本效益的有效性.
结论:
- 生活方式和人口因素是导致抑郁症的重要因素,特别是在COVID-19流行病等健康危机期间.
- 可扩展的在线调查为了解和减轻抑郁症风险因素提供了宝贵的工具.
- 这项研究为未来使用数字数据收集方法进行心理健康研究提供了强有力的管道.
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