在约旦使用千平方自动相互作用检测模型对妇女心脏病死亡率的有效预测:回顾性验证研究
Salam Bani Hani1, Muayyad Ahmad1
1Clinical Nursing Department, School of Nursing, The University of Jordan, Amman, Jordan.
JMIR cardio
|July 20, 2023
概括
这项研究使用机器学习来预测女性心脏病死亡率. 千平方自动交互检测模型准确地确定了女性心血管死亡的关键预测因素.
科学领域:
- 心血管疾病的研究研究.
- 医疗保健中的人工智能
- 在医学领域的数据挖掘.
背景情况:
- 妇女患心脏病的风险是一个重大的公共卫生问题.
- 机器学习算法 (MLA) 越来越多地用于分析大型数据集以进行健康预测.
- 确定妇女的特定死亡预测因素对于有针对性的干预措施至关重要.
研究的目的:
- 用基于人工智能的机器学习算法预测女性心脏病死亡率.
- 确定与女性心血管死亡率相关的关键变量.
主要方法:
- 对2028名被诊断患有心脏病的约旦妇女 (2015-2021) 电子健康记录的回顾性分析.
- 数据预处理包括清理,组织和消除冗余信息.
- 评估九个人工智能模型,以确定最准确的死亡率预测.
主要成果:
- 基平方自动相互作用检测 (CHAID) 模型实现了最高的准确性 (93.25%) 和AUC (0.825).
- 常见的诊断包括胸痛 (62.3%) 和充血性心力衰竭 (37.7%).
- 死亡率的关键预测因素是年龄,高静脉压 (>187 mm Hg),充血性心力衰竭诊断,脉冲压 (>98 mm Hg) 和低氧和 (<93%).
结论:
- 机器学习,特别是CHAID模型,使用电子健康记录数据有效预测女性的心血管死亡率.
- 该研究确定了临床临床预测死亡率的关键因素,为风险评估和临床决策提供了实用工具.
- 大数据分析可以提高对女性心脏病的理解和管理.
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