使用机器学习开发一个5年风险预测模型,从糖尿病前期转变为糖尿病:回顾性队列研究
Yongsheng Zhang1,2, Hongyu Zhang1,2, Dawei Wang1,2
1Department of Health Management, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
Journal of medical Internet research
|May 9, 2025
概括
一个新的机器学习模型准确地预测了中国糖尿病前期糖尿病患者糖尿病进展的5年风险. 这种工具有助于识别高风险患者进行早期干预,可能减少糖尿病发病率.
科学领域:
- 糖尿病研究研究 糖尿病研究
- 机器学习在医疗保健中的应用
- 公共卫生干预措施 公共卫生干预
背景情况:
- 糖尿病是一个全球性的健康危机,糖尿病前期代表了干预的关键阶段.
- 在中国的糖尿病前期患者中缺乏糖尿病进展的5年风险预测模型.
- 在糖尿病前期早期识别和干预可以减轻糖尿病发病率和医疗负担.
研究的目的:
- 开发和验证基于机器学习的5年风险预测模型,用于中国人群中糖尿病前期到糖尿病进展.
- 创建一个互动的基于网络的平台,用于识别高风险个体,并促进早期干预.
- 为了减少糖尿病的总体发病率和相关的医疗保健费用.
主要方法:
- 一个回顾性队列研究,涉及两个中国的糖尿病前期队列 (n=6578和n=2333) 从2019-2024.
- 利用42个人口,物理和血液学变量,应用递归特征消除和7个机器学习算法,包括CatBoost.
- 使用网格搜索和交叉验证的优化模型,通过ROC曲线,精度回忆曲线,精度,灵敏度和特异性来评估性能.
主要成果:
- 使用14个选定的特征,CatBoost模型表现出最佳性能,AUC为0.819 (测试组) 和0.807 (外部测试组).
- 该模型表现出优异的区分和校准,主要预测指标包括禁食血糖 (FBG),高血压,ALT/AST,BMI,年龄和MONO.
- 从糖尿病前期到糖尿病的年进展率分别在初级和外部队列中为8.34%和7.04%.
结论:
- 一个强大的5年风险预测模型,用于糖尿病前期和糖尿病进展,已成功为中国人群开发.
- CatBoost模型表现出最高的预测性能,有效地识别出患糖尿病高风险的个体.
- 开发的模型和已识别的预测因素为预防糖尿病提供了有针对性的干预措施的途径.
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