用深度学习预测心梗致命后果并识别关键生物标志物。
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
- 融合多种机器学习模型与神经网络，自动预测心梗死亡风险。
- 在临床数据上实现高精度预测，关键指标召回率达89.3%。
- 帮助医生快速识别危险因素，适合临床辅助诊断场景。
心血管疾病仍是全球主要死因之一。急性心肌梗死（MI）每年夺走数百万生命，因冠状动脉血流受阻导致心肌永久损伤。若不及时治疗，可引发心脏骤停、器官衰竭甚至死亡。研究显示，约5%至10%的幸存者在一年内死亡，近半数需再次住院。早期溶栓治疗可显著改善预后，因此亟需更快速准确的诊断方法。目前医生多依赖病史和经验判断，耗时且主观性强。本研究提出一种自动化模型，用于预测心梗致命结局，并识别相关关键生物标志物。数据预处理采用SVMSMOTE、ADASYN及类别加权法处理不平衡数据；通过包裹式与嵌入式特征选择确定核心变量，并进行标准化。模型结合逻辑回归、随机森林、Light-GBM、Bagging SVM，并进一步以人工神经网络优化性能。所有模型均基于精确率、召回率等指标评估，筛选出最适合临床应用的方案。
原文摘要 · Abstract (English)
Cardiovascular disease is still one of the main causes of death around the world. Acute myocardial infarction (MI), or heart attack, claims millions of lives each year. MI happens when blood flow to the coronary arteries is blocked or reduced, which causes permanent damage to the heart muscle. Without treatment, this can lead to cardiac arrest, where the heart stops pumping blood to the organs, resulting in organ failure and death. Even survivors often face serious problems like heart failure, pulmonary edema, and asystole. Research shows that 5 to 10 percent of survivors die within the first year after an MI, and nearly half need to be hospitalized again. Early thrombolytic treatment leads to better outcomes, so there is a clear need for faster and more accurate ways to diagnose MI. Right now, doctors usually review patient history and use their own experience to find the causes of MI. This process takes a lot of time and can be inconsistent. Detecting MI accurately and quickly can help patients take better care of themselves and prevent fatal events. In this study, we introduce an automated model to predict deadly outcomes of MI and help doctors understand important biomarkers linked to its complications. This approach aims to make diagnosis clearer, faster, and more affordable. The process includes preparing the data, filling in missing values, and handling imbalanced data using SVMSMOTE, ADASYN, and class-weighted methods. We use wrapper and embedded feature selection to find the most important variables, then scale the features for consistency. The model combines Logistic Regression, Random Forest, Light-GBM, and Bagging SVM, and is further improved with an artificial neural network to increase accuracy. We evaluate all models using precision, recall, and other key measures to find the best option for clinical use.
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