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机器学习算法用于预测糖尿病患者的主要不良心血管事件

Tadesse M Abegaz¹, Ahmead Baljoon¹, Oluwaseun Kilanko¹

¹Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL, 32307, USA.

Computers in biology and medicine

|August 9, 2023

概括

机器学习准确地预测了2型糖尿病患者的主要不良心血管事件 (MACE). 酸盐和血尿酸水平是MACE风险的关键指标.

科学领域:

心血管医学心血管医学
内分泌学在内分泌学.
数据科学数据科学数据科学

背景情况:

主要不良心血管事件 (MACE),包括心肌梗塞 (MI),中风和心力衰竭 (HF),是2型糖尿病 (T2DM) 的常见并发症.
预测MACE对于管理T2DM患者和减轻心血管风险至关重要.

研究的目的:

开发和评估用于预测T2DM患者MACE的机器学习 (ML) 模型.
确定与MACE预测相关的关键临床属性,电解质和生物标志物.

主要方法:

从"我们所有人"研究计划中招募T2DM患者 (年龄大于18岁),他们正在服用-葡萄糖共运输体2抑制剂.
应用的ML算法:RandomForest (RF),XGBoost,逻辑回归 (LR) 和加权集团模型 (WEM).
使用平均下降准确度确定特征重要性,以确定关键预测因素.

主要成果:

XGBoost模型实现了最高的预测准确度 (0.80 [0.78-0.82]),超过了RF (0.78),LR (0.65) 和WEM (0.75).
预测中风的分类准确度超过95%,其次是MI (~85%) 和HF (~80%).
酸盐,血尿素和热波水平被确定为MACE的主要预测因素.

结论:

ML模型,特别是XGBoost,在T2DM患者中有效预测MACE.
酸盐,血尿素和其他电解质是MACE的显著预测因素,在中风,心脏病发作和高血压中一致.
这些已识别的参数可以作为T2DM中MACE事件的预后标记.

关键词:

糖尿病糖尿病糖尿病心脏衰竭是因为心脏衰竭.机器学习机器学习心肌梗塞的心脏病发作预测预测预测一次性中风,中风.

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