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使用机器学习预测糖尿病管理中的30天再入院率

Vinaytosh Mishra¹, Mohan R Tanniru², Jayadevan Sreedharan³

¹Thumbay College of Management and AI in Healthcare, Gulf Medical University, Ajman, United Arab Emirates.

Computers in biology and medicine

|June 21, 2025

概括

机器学习模型可以预测30天的糖尿病再入院. XGBoost显示出最好的精度,回忆和F1得分,而随机森林在较小数据集的准确度方面表现出色.

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
糖尿病管理糖尿病管理

背景情况:

糖尿病是一种慢性疾病,需要复杂的护理,往往导致频繁的医院再入院.
预测30天的再入院对于积极的患者管理和减少医疗保健负担至关重要.
识别高风险患者使得有针对性的干预措施能够改善结果.

研究的目的:

开发和评估机器学习模型,用于预测糖尿病患者的30天再入院情况.
为了比较物流回归,决策树,随机森林和XGBoost算法的性能.
确定最有效的模型来预测与糖尿病相关的再入院.

主要方法:

利用来自印度瓦拉纳西的一家糖尿病专科诊所的352个记录的数据集.
开发了使用物流回归,决策树,随机森林和XGBoost的预测模型.
使用精度,回忆,F1得分和AUC-ROC指标评估模型性能.

主要成果:

XGBoost实现了最高的精度 (0.84),回忆 (0.87),以及F1得分 (0.85).
随机森林的AUC-ROC值高于0.94,表明强大的检测能力.
XGBoost 提供了更高的预测准确度,而随机森林对较小数据集的过度匹配更具稳定性.

结论:

机器学习模型,特别是XGBoost和随机森林,可以有效地预测30天的糖尿病再入院.
模型选择至关重要,XGBoost在整体准确度方面表现出色,随机森林适合较小的数据集.
这些预测能力使医疗保健提供者能够及时实施干预措施,增强患者护理并降低再接收率.

关键词:

30天的再接收时间.决策树决策树是一个决策树.糖尿病糖尿病糖尿病后勤回归的逻辑回归机器学习是机器学习.随机的森林随机的森林在XGBoost中使用.

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