机器学习技术应用于预测暴饮暴食的开始和持续:一项前性研究
Zoe McClure1, Christopher J Greenwood1,2,3,4, Matthew Fuller-Tyszkiewicz1,2
1School of Psychology, Deakin University, Geelong, Australia.
概括
机器学习模型在预测暴饮暴食的发作和持续性方面表现不佳. 未来的研究需要更多不同的数据类型超越自我报告,以更好地预测饮食行为.
科学领域:
- 精神病学是一个精神病学.
- 计算精神病学是一种计算精神病学.
- 行为科学 行为科学
背景情况:
- 机器学习 (ML) 显示出预测临床结果的潜力.
- 应用ML来预测暴饮暴食行为是未经探索的.
研究的目的:
- 应用ML技术来预测暴饮暴食的开始和持续.
- 将ML模型性能与通用线性模型 (GLM) 进行比较.
主要方法:
- 利用了来自1106名参与者的数据,进行了基线和8个月后续评估.
- 开发了9个ML分类模型,使用39个自我报告的基线变量.
- 将ML模型与GLM进行比较,以预测发病和持续性.
主要成果:
- 所有模型在预测暴饮暴食发作方面表现不佳 (AUC 0.49-0.61).
- 模型还显示了预测暴饮暴食持续性的表现不佳 (AUC 0.50-0.59).
- ML模型的性能与GLM相比.
结论:
- 有限的自我报告基线预测因素可能导致了ML模型性能差.
- 未来的研究应该纳入更广泛的预测因素,包括神经成像和传感器数据.
- 优化ML模型需要不同的数据类型,以提高饮食行为研究中的预测准确度.
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