Abstract
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.
Abstract (translated)
冠状动脉疾病（CAD）仍然是全球发病率和死亡率的主要原因。早期检测对于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）的进步在提高CAD诊断准确性方面显示出巨大潜力。本研究探讨了应用机器学习算法通过分析包括临床特征、影像学和生物标志物在内的患者数据来改进CAD检测的方法。双向长短期记忆网络（Bi-LSTM）、门控循环单元（GRU）以及Bi-LSTM与GRU的混合模型被训练在大规模数据集上预测CAD的存在情况。结果显示，这些机器学习模型在敏感性和特异性方面优于传统诊断方法，为临床医生提供了更有力的决策工具。实验结果表明，混合模型实现了97.07%的准确性。通过整合先进的数据预处理技术和特征选择技术，本研究确保了最佳的学习效果和模型性能，为机器学习在CAD诊断中的应用设立了新的标准。将机器学习集成到CAD检测中为个性化医疗提供了有前景的方向，并可能在未来心血管疾病的管理中发挥关键作用。
URL
https://arxiv.org/abs/2603.06888
