预测约旦男性心脏病发作的死亡率,使用千方形自动交互检测模型.
Salam Bani Hani1, Muayyad Ahmad2
1School of Nursing, Nursing Department, Irbid National University, Irbid, Jordan.
Health informatics journal
|August 8, 2024
概括
这项研究开发了一种机器学习模型,用于预测男性心脏病发作死亡率. 千平方自动交互检测 (CHAID) 模型实现了93.72%的准确性,识别了改善患者结果的关键风险因素.
科学领域:
- 心血管疾病研究研究
- 医疗保健中的机器学习
- 公共卫生信息学 公共卫生信息学
背景情况:
- 心脏病发作是全球主要的心血管疾病之一.
- 男人不成比例地受到心脏病的影响.
- 准确预测心脏病发作的结果对于拯救生命至关重要.
研究的目的:
- 提出和评估千平方自动交互检测 (CHAID) 模型,用于预测心脏病发作男性的死亡率.
- 在约旦男性人口中确定心脏病发作死亡率的重大风险因素.
主要方法:
- 使用约旦电子健康解决方案系统 (2015-2021) 的数据进行回顾性,预测性研究设计.
- 从公立医院接受的男性患者收集的信息.
- 应用CHAID算法用于预测建模.
主要成果:
- CHAID模型显示出高预测准确度 (93.72%) 和曲线下的面积 (AUC) 为0.792.2.
- 死亡率的关键预测因素包括省份,年龄,脉冲氧计,医学诊断,脉冲压,心率和缩血压.
- 确定CHAID模型是预测这一队列中的死亡率的最佳模型.
结论:
- 人口特征和血液动力学读数是心脏病发作死亡率的重要预测因素.
- 机器学习算法,特别是CHAID模型,可以有效预测心脏病发作患者的死亡风险.
- 这些发现强调了CHAID模型在改善心血管疾病管理中的患者结果方面的实用性.
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