School of Nursing, Nursing Department, Irbid National University, Irbid, Jordan.
School of Nursing, Clinical Nursing Department, University of Jordan, Ammanm, Jordan.
Health Informatics J. 2024 Jul-Sep;30(3):14604582241270830. doi: 10.1177/14604582241270830.
One of the most complicated cardiovascular diseases in the world is heart attack. Since men are the most likely to develop cardiac diseases, accurate prediction of these conditions can help save lives in this population. This study proposed the Chi-Squared Automated Interactive Detection (CHAID) model as a prediction algorithm to forecast death versus life among men who might experience heart attacks. Data were extracted from the electronic health solution system in Jordan using a retrospective, predictive study. Between 2015 and 2021, information on men admitted to public hospitals in Jordan was gathered. The CHAID algorithm had a higher accuracy of 93.72% and an area under the curve of 0.792, making it the best top model created to predict mortality among Jordanian men. It was discovered that among Jordanian men, governorates, age, pulse oximetry, medical diagnosis, pulse pressure, heart rate, systolic blood pressure, and pulse pressure were the most significant predicted risk factors of mortality from heart attack. With heart attack complaints as the primary risk factors that were predicted using machine learning algorithms like the CHAID model, demographic characteristics and hemodynamic readings were presented.
世界上最复杂的心血管疾病之一是心脏病发作。由于男性最容易患心脏疾病，因此准确预测这些疾病可以帮助挽救该人群的生命。本研究提出了卡方自动交互检测（CHAID）模型作为预测算法，以预测可能发生心脏病发作的男性的死亡与生存情况。 使用回顾性预测研究，从约旦的电子健康解决方案系统中提取数据。2015 年至 2021 年期间，收集了约旦公立医院收治的男性信息。CHAID 算法的准确率为 93.72%，曲线下面积为 0.792，是创建来预测约旦男性死亡率的最佳顶级模型。研究发现，在约旦男性中，省份、年龄、脉搏血氧饱和度、医疗诊断、脉压、心率、收缩压和脉压是心脏病发作导致死亡的最重要预测风险因素。 利用机器学习算法（如 CHAID 模型）预测的心脏病发作投诉等主要风险因素，提出了人口统计学特征和血液动力学读数。
