通过基于机器学习的可解释风险模型识别超重人口中的影响因素:一个大型追溯队列
Wei Lin1, Songchang Shi2, Huiyu Lan3
1Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China. caolalin0929@163.com.
机器学习有效地识别了超重的风险因素. CatBoost,结合Shapley增量解释,为公共卫生干预提供了卓越的预测和解释能力.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 识别超重风险因素对于预测健康风险和指导干预至关重要.
- 机器学习模型面临着诸如过拟合和解释性差等挑战.
研究的目的:
- 构建和验证用于预测超重风险的机器学习模型.
- 为此目的确定最佳的机器学习方法.
- 使用可解释性技术增强模型的解释性.
主要方法:
- 采用九种常见的机器学习算法来构建超重风险模型.
- 利用了福建省宁德市10905名中国受试者的数据.
- 应用严格的验证和验证来选择表现最佳的模型.
主要成果:
- 机器学习模型在预测超重风险方面表现强.
- 与其他方法相比,CatBoost成为一种潜在的优越方法.
- 沙普利添加式解释 (SHAP) 值为变量影响提供了明确的见解.
结论:
- 机器学习为开发超重风险模型提供了一个强大的方法.
- CatBoost是一个先进的机器学习技术,用于临床风险建模.
- 将SHAP与机器学习相结合,可以更好地识别疾病风险因素以进行预防.
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