基于AI机器学习的韩国老年糖尿病预测:横截面分析
Hocheol Lee1, Myung-Bae Park1, Young-Joo Won1
11, Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Changjogwan, Yonseidae-gil 1, Wonju, 26493, Republic of Korea, +82 (0) 33-760-2257.
JMIR formative research
|January 22, 2025
概括
机器学习可以准确地预测老年人患糖尿病的情况,并将高血压,年龄和压力确定为关键风险因素. 这有助于开发针对性干预措施,以预防和管理糖尿病.
科学领域:
- 老年学是一门学科.
- 计算医学是一种计算医学.
- 公共卫生 公共卫生
背景情况:
- 糖尿病对老年人来说是一个重要的健康问题.
- 机器学习为预测这种人口中的糖尿病提供了潜力.
研究的目的:
- 在60岁及以上的成年人中使用机器学习识别糖尿病风险因素.
- 开发和优化老年糖尿病的预测模型.
主要方法:
- 一项对3084名首尔老年人的横截面研究.
- 通过移动应用程序和健康协调员收集的数据,包括生活方式和临床因素.
- 使用多个机器学习算法 (随机森林,GBM,LGBM,XGBM,KNN) 进行分析,并使用SHAP进行解释.
主要成果:
- 高血压,高脂血症,年龄,压力和心率是糖尿病的重要预测因素.
- 极端梯度增强模型 (XGBM) 获得了最高的性能 (84.88%准确率,0.7957 AUC).
- SHAP分析确定高血压,年龄,身体脂肪,心率,高脂血症,BMR,压力和氧和为关键预测因素.
结论:
- 这项研究强调了老年人糖尿病的可修改风险因素.
- 研究结果支持开发用于主动护理的自动化健康数据收集系统.
- 机器学习模型为糖尿病风险分层和干预提供了宝贵的见解.
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