Ghafari Reza, Azar Amir Sorayaie, Ghafari Ali, Aghdam Fatemeh Moradabadi, Valizadeh Morteza, Khalili Naser, Hatamkhani Shima
Pharmacy Faculty, Urmia University of Medical Sciences, Urmia, Iran.
Department of Computer Engineering, Urmia University, Urmia, Iran.
J Tehran Heart Cent. 2023 Oct;18(4):278-287. doi: 10.18502/jthc.v18i4.14827.
Myocardial infarction (MI) is a major cause of death, particularly during the first year. The avoidance of potentially fatal outcomes requires expeditious preventative steps. Machine learning (ML) is a subfield of artificial intelligence science that detects the underlying patterns of available big data for modeling them. This study aimed to establish an ML model with numerous features to predict the fatal complications of MI during the first 72 hours of hospital admission.
We applied an MI complications database that contains the demographic and clinical records of patients during the 3 days of admission based on 2 output classes: dead due to the known complications of MI and alive. We utilized the recursive feature elimination (RFE) method to apply feature selection. Thus, after applying this method, we reduced the number of features to 50. The performance of 4 common ML classifier algorithms, namely logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost), was evaluated using 8 classification metrics (sensitivity, specificity, precision, false-positive rate, false-negative rate, accuracy, F1-score, and AUC).
In this study of 1699 patients with confirmed MI, 15.94% experienced fatal complications, and the rest remained alive. The XGBoost model achieved more desirable results based on the accuracy and F1-score metrics and distinguished patients with fatal complications from surviving ones (AUC=78.65%, sensitivity=94.35%, accuracy=91.47%, and F1-score=95.14%). Cardiogenic shock was the most significant feature influencing the prediction of the XGBoost algorithm.
XGBoost algorithms can be a promising model for predicting fatal complications following MI.
心肌梗死（MI）是主要的死亡原因，尤其是在第一年。避免潜在的致命后果需要迅速采取预防措施。机器学习（ML）是人工智能科学的一个子领域，它能检测可用大数据的潜在模式并进行建模。本研究旨在建立一个具有众多特征的ML模型，以预测心肌梗死患者入院后72小时内的致命并发症。
我们应用了一个心肌梗死并发症数据库，该数据库包含患者入院3天内的人口统计学和临床记录，基于2个输出类别：因已知的心肌梗死并发症死亡和存活。我们利用递归特征消除（RFE）方法进行特征选择。应用此方法后，我们将特征数量减少到50个。使用8种分类指标（灵敏度、特异性、精度、假阳性率、假阴性率、准确率、F1分数和AUC）评估了4种常见的ML分类器算法，即逻辑回归、支持向量机、随机森林和极端梯度提升（XGBoost）的性能。
在这项对1699例确诊心肌梗死患者的研究中，15.94%发生了致命并发症，其余患者存活。基于准确率和F1分数指标，XGBoost模型取得了更理想的结果，并区分了有致命并发症的患者和存活患者（AUC = 78.65%，灵敏度 = 94.35%，准确率 = 91.47%，F1分数 = 95.14%）。心源性休克是影响XGBoost算法预测的最显著特征。
XGBoost算法可能是预测心肌梗死后致命并发症的一个有前景的模型。
