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机器学习模型的开发和验证,用于识别糖尿病前期和糖尿病在正常血糖症中
Xiaodong Zhang1, Weidong Yao2, Dawei Wang3
1Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China.
Diabetes/metabolism research and reviews
|November 5, 2024
概括
机器学习模型可以识别正常禁食血糖的个体中的异常葡萄糖代谢 (AGM). 这种方法有助于早期发现和干预糖尿病前期和糖尿病,改善患者的治疗结果.
科学领域:
- 生物医学信息学 生物医学信息学
- 在医疗保健中的数据科学.
- 代谢性疾病研究研究
背景情况:
- 葡萄糖代谢异常 (AGM),包括糖尿病前期和糖尿病,对健康构成重大风险.
- 目前的查方法,如禁食血糖 (FBG),可能会错过患有AGM但FBG水平正常的个体.
- 早期识别AGM对于及时干预和预防并发症至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于识别正常血糖症患者的AGM.
- 利用例行健康检查指标来预测AGM.
- 改进超越传统查的AGM早期检测.
主要方法:
- 利用了来自59259名参与者的数据 (2019-2023年),使用美国糖尿病协会 (ADA) 标准和糖化血红蛋白 (HbA1c) 值来定义AGM.
- 在培训,内部和外部数据集上训练和验证了七个ML算法 (包括CatBoost,Random Forest,SVM).
- 评估模型性能使用接收器操作特征曲线 (auROC) 和精度回忆曲线 (auPR) 下的面积,用SHapley添加式扩展 (SHAP) 评估特征重要性.
主要成果:
- CatBoost模型表现出卓越的性能,实现了0.806的auROC (内部验证) 和0.794 (外部验证).
- 关键的预测特征包括年龄,禁食血糖,红细胞,血红蛋白,体重指数和甘油三糖水平.
- 基于最佳的ML模型,开发了一个用户友好的交互界面.
结论:
- 一个强大的ML模型有效地识别了正常葡萄糖代谢异常的个体.
- 该模型为早期AGM检测提供了有价值的工具,为可能错过的病例提供了及时的干预.
- 这些发现凸显了ML在加强对代谢障碍的公共卫生查方面的潜力.
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