Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Siming District, Xiamen, Fujian 361005, China.
J Proteome Res. 2023 Mar 3;22(3):758-767. doi: 10.1021/acs.jproteome.2c00488. Epub 2023 Jan 30.
The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, 1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases.
急性心肌梗死（AMI）患者的风险分层对临床管理和预后评估至关重要。因此，我们提出了一种名为 ADASYN-RFECV-MDA-DNN（ARMD）的集成机器学习分析程序，以解决样本不平衡问题，并实现 AMI 结局的分层和预测。ARMD 分析程序应用于来自 534 名 AMI 相关受试者的 NMR 血清数据，这些受试者分为四个类别，样本比例极不平衡。首先，使用自适应合成采样（ADASYN）算法解决原始样本不平衡的问题。其次，采用递归特征消除与交叉验证（RFECV）处理和随机森林平均减少精度（RF-MDA）算法识别与每种 AMI 结局相关的差异代谢物。最后，使用深度神经网络（DNN）对 AMI 事件进行分类和预测，并通过比较四种传统机器学习方法来评估其性能。与其他四种机器学习模型相比，DNN 在几乎所有模型参数（包括精度、1 分、敏感性、特异性、接收器操作特征曲线（AUC）下的面积和分类准确性）中均表现出一致的优势，突出了深度学习在临床疾病分类和分层中的潜力。ARMD 分析程序是一种用于临床疾病监督分类和回归建模的实用分析工具。
