基于营养和生活方式变量预测成年男性的压力,焦虑和抑郁:机器学习方法的比较分析
Tayeb Mohammadi1, Sara Orouei2, Karim Parastouei3
1Student Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
机器学习模型有效地利用饮食和生活方式数据预测成年男性的抑郁,焦虑和压力 (DAS). 关键因素包括油炸食品,体力活动,BMI以及和等营养素.
科学领域:
- 营养精神病学 营养精神病学
- 计算健康科学 计算健康科学
背景情况:
- 全球精神健康障碍的增加,如抑郁,焦虑和压力 (DAS).
- 饮食和生活方式是影响精神健康的关键可修改因素.
- 需要先进的预测工具来识别风险因素和指导干预措施.
研究的目的:
- 调查机器学习 (ML) 模型在预测成年男性DAS中的有效性.
- 确定DAS的主要饮食和生活方式预测因素.
- 探索ML在个性化心理健康干预方面的潜力.
主要方法:
- 用食物频率问卷 (FFQ) 数据对400名成年男性进行横截面研究.
- 分析了59个预测变量,将DAS分类为正常或无序.
- 使用交叉验证和性能指标 (灵敏度,特异性,精度,AUC) 评估了五种ML模型 (包装,提升,天真贝叶斯,SVM,RF).
主要成果:
- 在25.47%的参与者中存在DAS.
- 包装,增强和随机森林 (RF) 模型显示出优异的预测性能 (>70%跨度指标).
- 重要的预测因素包括炸快餐,体力活动 (PA),体重指数 (BMI),,,咖啡因和胆固醇.
结论:
- 基于饮食和生活方式因素,ML算法,特别是RF,提升和包装,在预测DAS方面是有效的.
- 饮食成分 (,,咖啡因) 和生活方式因素 (PA,BMI,胆固醇) 是心理健康状况的重要预测因素.
- 研究结果支持开发有针对性的营养和生活方式干预措施,以改善心理健康.
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