Sajid Muhammad, Hassan Ali, Khan Dilshad Ahmed, Khan Shoab Ahmed, Bakhshi Asim Dilawar, Akram Muhammad Usman, Babar Mishal, Hussain Farhan, Abdul Wadood
IEEE J Biomed Health Inform. 2024 Dec;28(12):7543-7552. doi: 10.1109/JBHI.2024.3453911. Epub 2024 Dec 5.
Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest, accounting for a large percentage of global mortality. A timely diagnosis and detection may save a person's life. The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography. Novel clinical, chemical, and molecular cardiac biomarkers were used as input features from an especially collected dataset. Following a thorough evaluative search in the biomarker feature space, the optimum parameters for classifier or regression technique (regressor) were selected using K-fold cross-validation. Ten machine learning (ML) classifiers were employed in classification tasks to determine the number of affected cardiac vessels, the Gensini group, and the severity of CAD with 82.58%, 86.26%, and 90.91% accuracy, respectively. Eleven approaches were used in regression tasks to calculate stenosis percentage and Gensini score, with R-squared values of 0.58 and 0.56, respectively. Following a thorough evaluative search in the biomarkers feature space, the optimum feature and classifier or regressor set were selected using K-fold cross-validation. The biomarkers and classifier or regressor combinations serve as the foundation for the proposed risk stratification framework, incorporating clinical protocol. Finally, our proposed framework is compared to state-of-the-art studies, offering a robust, well-rounded, early detection capable, and novel 'biomarkers-ML combination' approach to risk stratification.
冠状动脉疾病（CAD）是心脏骤停最常见的原因之一，在全球死亡率中占很大比例。及时诊断和检测可能挽救一个人的生命。该研究提出了一种基于仅在有创冠状动脉造影后才可能获得的信息进行非侵入性风险分层的方法框架。将新型临床、化学和分子心脏生物标志物用作来自特别收集数据集的输入特征。在生物标志物特征空间进行全面评估搜索后，使用K折交叉验证选择分类器或回归技术（回归器）的最佳参数。在分类任务中采用了十种机器学习（ML）分类器来确定受影响的心脏血管数量、Gensini分组以及CAD的严重程度，准确率分别为82.58%、86.26%和90.91%。在回归任务中使用了十一种方法来计算狭窄百分比和Gensini评分，R平方值分别为0.58和0.56。在生物标志物特征空间进行全面评估搜索后，使用K折交叉验证选择最佳特征以及分类器或回归器集。生物标志物与分类器或回归器的组合构成了所提出的风险分层框架的基础，并纳入了临床方案。最后，将我们提出的框架与最先进的研究进行比较，提供了一种强大、全面、具备早期检测能力且新颖的“生物标志物 - 机器学习组合”风险分层方法。
