用尿液肽段和神经网络，95%以上准确预测冠心病。
Peptidomic-Based Prediction Model for Coronary Heart Disease Using a Multilayer Perceptron Neural Network
- 基于遗传算法选50个尿肽标志物，用三层感知机建模。
- 模型在345人组上达到95.67%准确率，AUC达0.9748。
- 适合做无创冠心病筛查，尤其对早期诊断有帮助。
冠心病（CHD）是全球主要死因之一，显著增加年度医疗支出。为开发无创诊断方法，我们基于多层感知机（MLP）神经网络设计了一种模型，使用遗传算法筛选出50个关键尿液肽生物标志物进行训练。治疗组与对照组各含345名个体，通过合成少数类过采样技术（SMOTE）实现平衡。采用分层验证策略训练神经网络，网络结构包含三个隐藏层，每层60个神经元，输出层2个神经元。模型精度、敏感性和特异性均达95.67%，F1分数为0.9565。两个类别下的受试者工作特征曲线下面积（AUC）均为0.9748，马修斯相关系数（MCC）和克朗巴赫系数（Cohen's kappa）分别为0.9134和0.9131，表明该模型在冠心病检测中具有高度可靠性和鲁棒性。结果表明，该模型可作为一种高精度、强鲁棒性的无创冠心病诊断工具。
原文摘要 · Abstract (English)
Coronary heart disease (CHD) is a leading cause of death worldwide and contributes significantly to annual healthcare expenditures. To develop a non-invasive diagnostic approach, we designed a model based on a multilayer perceptron (MLP) neural network, trained on 50 key urinary peptide biomarkers selected via genetic algorithms. Treatment and control groups, each comprising 345 individuals, were balanced using the Synthetic Minority Over-sampling Technique (SMOTE). The neural network was trained using a stratified validation strategy. Using a network with three hidden layers of 60 neurons each and an output layer of two neurons, the model achieved a precision, sensitivity, and specificity of 95.67 percent, with an F1-score of 0.9565. The area under the ROC curve (AUC) reached 0.9748 for both classes, while the Matthews correlation coefficient (MCC) and Cohen's kappa coefficient were 0.9134 and 0.9131, respectively, demonstrating its reliability in detecting CHD. These results indicate that the model provides a highly accurate and robust non-invasive diagnostic tool for coronary heart disease.
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