Department of Cardiovascular Medicine, The Heart Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
Zhejiang Suosi Technology Co. Ltd, Wenzhou, 325000, Zhejiang, China.
Comput Biol Med. 2024 Mar;170:107950. doi: 10.1016/j.compbiomed.2024.107950. Epub 2024 Jan 2.
Acute ST-segment elevation myocardial infarction (STEMI) is a severe cardiac ailment characterized by the sudden complete blockage of a portion of the coronary artery, leading to the interruption of blood supply to the myocardium. This study examines the medical records of 3205 STEMI patients admitted to the coronary care unit of the First Affiliated Hospital of Wenzhou Medical University from January 2014 to December 2021. In this research, a novel predictive framework for STEMI is proposed, incorporating evolutionary computational methods and machine learning techniques. A variant algorithm, AGCOSCA, is introduced by integrating crossover operation and observation bee strategy into the original Sine Cosine Algorithm (SCA). The effectiveness of AGCOSCA is initially validated using IEEE CEC 2017 benchmark functions, demonstrating its ability to mitigate the deficiency in local mining after SCA random perturbation. Building upon this foundation, the AGCOSCA approach has been paired with Support Vector Machine (SVM) to forge the predictive framework referred to as AGCOSCA-SVM. Specifically, AGCOSCA is employed to refine the selection of predictors from a substantial feature set before SVM is utilized to forecast the occurrence of STEMI. In our analysis, we observed that SVM excels at managing nonlinear data relationships, a strength that becomes particularly prominent in smaller datasets of STEMI patients. To assess the effectiveness of AGCOSCA-SVM, diagnostic experiments were conducted based on the STEMI sample data. Results indicate that AGCOSCA-SVM outperforms traditional machine learning methods, achieving superior Accuracy, Sensitivity, and Specificity values of 97.83 %, 93.75 %, and 96.67 %, respectively. The selected features, such as acute kidney injury (AKI) stage, fibrinogen, mean platelet volume (MPV), free triiodothyronine (FT3), diuretics, and Killip class during hospitalization, are identified as crucial for predicting STEMI. In conclusion, AGCOSCA-SVM emerges as a promising model framework for supporting the diagnostic process of STEMI, showcasing potential applications in clinical settings.
急性 ST 段抬高型心肌梗死（STEMI）是一种严重的心脏疾病，其特征是冠状动脉的一部分突然完全阻塞，导致心肌的血液供应中断。本研究检查了 2014 年 1 月至 2021 年 12 月期间温州医科大学第一附属医院冠心病监护病房收治的 3205 例 STEMI 患者的病历。在这项研究中，提出了一种新的 STEMI 预测框架，该框架结合了进化计算方法和机器学习技术。通过将交叉操作和观察蜂策略集成到原始正弦余弦算法（SCA）中，引入了一种变体算法 AGCOSCA。AGCOSCA 的有效性最初通过使用 IEEE CEC 2017 基准函数进行验证，证明其能够减轻 SCA 随机扰动后局部挖掘的不足。在此基础上，将 AGCOSCA 方法与支持向量机（SVM）相结合，构建了称为 AGCOSCA-SVM 的预测框架。具体来说，AGCOSCA 用于从大量特征集中选择预测器，然后使用 SVM 预测 STEMI 的发生。在我们的分析中，我们观察到 SVM 擅长处理非线性数据关系，这在 STEMI 患者的较小数据集上尤为明显。为了评估 AGCOSCA-SVM 的有效性，我们基于 STEMI 样本数据进行了诊断实验。结果表明，AGCOSCA-SVM 优于传统的机器学习方法，其准确率、敏感度和特异性分别达到 97.83%、93.75%和 96.67%。选定的特征，如急性肾损伤（AKI）阶段、纤维蛋白原、平均血小板体积（MPV）、游离三碘甲状腺原氨酸（FT3）、利尿剂和住院期间的 Killip 分级，被确定为预测 STEMI 的关键特征。总之，AGCOSCA-SVM 是一种有前途的模型框架，可用于支持 STEMI 的诊断过程，在临床环境中具有潜在的应用前景。
