用机器学习提升冠心病早期检测准确率，最高达97.07%。
Enhancing the Detection of Coronary Artery Disease Using Machine Learning
- 融合临床、影像与生物标志物数据，用双向LSTM和GRU模型分析
- 混合模型准确率达97.07%，显著优于传统诊断方法
- 适合心血管疾病研究者与临床辅助诊断系统开发者
冠状动脉疾病（CAD）仍是全球致病和致死的主要原因。早期检测对改善患者预后和降低医疗成本至关重要。近年来，机器学习（ML）在提升CAD诊断准确性方面展现出巨大潜力。本研究探讨了机器学习算法在分析患者临床特征、影像数据及生物标志物谱的基础上，用于改进CAD检测的应用。采用双向长短期记忆网络（Bi-LSTM）、门控循环单元（GRU）以及二者混合模型，在大规模数据集上进行训练以预测CAD存在与否。实验结果表明，这些机器学习模型在敏感性和特异性方面均优于传统诊断方法，为临床医生提供更可靠的决策支持工具。其中，混合模型达到97.07%的准确率。通过结合先进的数据预处理与特征选择技术，本研究确保了模型最优学习性能，为机器学习在CAD诊断中的应用设立了新基准。将机器学习融入CAD检测，为个性化医疗提供了前景，并可能在未来心血管疾病管理中发挥关键作用。
原文摘要 · Abstract (English)
Coronary Artery Disease (CAD) remains a leading cause of morbidity and mortality worldwide. Early detection is critical to recover patient outcomes and decrease healthcare costs. In recent years, machine learning (ML) advancements have shown significant potential in enhancing the accuracy of CAD diagnosis. This study investigates the application of ML algorithms to improve the detection of CAD by analyzing patient data, including clinical features, imaging, and biomarker profiles. Bi-directional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and a hybrid of Bi-LSTM+GRU were trained on large datasets to predict the presence of CAD. Results demonstrated that these ML models outperformed traditional diagnostic methods in sensitivity and specificity, offering a robust tool for clinicians to make more informed decisions. The experimental results show that the hybrid model achieved an accuracy of 97.07%. By integrating advanced data preprocessing techniques and feature selection, this study ensures optimal learning and model performance, setting a benchmark for the application of ML in CAD diagnosis. The integration of ML into CAD detection presents a promising avenue for personalized healthcare and could play a pivotal role in the future of cardiovascular disease management.
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