College of Information and Computer (College of Data Science), Taiyuan University of Technology

【目的】心力衰竭简称心衰,是一种复杂的临床综合征,具有高发病率、高死亡率和预后效果不佳等显著特点,是各类心脏疾病发展的终末期,严重危害人类健康。因此,对心衰患者进行早期的预后评估研究至关重要,可以最大程度地帮助患者生存。【方法】提出一种基于多核支持向量机(multikernelsupportvectormachine,MK-SVM)和自适应提升算法(adaptiveboosting,AdaBoost)的心力衰竭死亡率评估模型(MK-SVM-AdaBoost).该算法利用MK-SVM将特征映射到高维空间,并依据AdaBoost算法将基本分类器进行集成,实现死亡率的精确预测。同时,将合成少数过采样技术(syntheticminorityoversamplingtechnique,SMOTE)和Tomeklinks欠采样技术相结合的混合抽样方法引入到预测模型中,减轻不平衡数据集对模型性能的影响。【结果】在收集于白求恩医院的小型心衰数据集上进行心衰患者30d内死亡率预测实验。实验结果表明,MK-SVM-AdaBoost模型的准确率和召回率分别达到了85.63%和86.33%,优于现有方法,ROC曲线下与坐标轴围成的面积(areaundercurve,AUC)和其微观平均值(micro-meanAUC,MiA-AUC)分别达到了91.00%和92.00%,表明提出的模型具有良好的稳定性。【结论】提出的模型具有较高的准确率和稳定性,可以为医生的临床决策提供一定的参考。今后课题将继续对数据集进行扩充,并对分级预警进行研究,以便对患者进行更有效的评估。

【Purposes】 Heart failure is a complex clinical syndrome with significant features such as high morbidity, high mortality, and poor prognosis. It is the terminal stage in the devel-opment of all types of heart disease and seriously threatens human health. Therefore, early prog-nostic assessment studies of heart failure patients are crucial to help the survival of patients. 【Methods】 A heart failure mortality assessment model (MK-SVM-AdaBoost) based on Multi Kernel Support Vector Machine (MK-SVM) and Adaptive Boosting (AdaBoost) algorithm is pro-posed. The algorithm utilizes MK-SVM to map features into a high-dimensional space and in-tegrates basic classifiers on the basis of the AdaBoost algorithm to achieve accurate mortality pre-diction. Meanwhile, a hybrid sampling method combining Synthetic Minority Oversampling Technique (SMOTE) and Tomek links under-sampling technique is introduced into the prediction model to alleviate the impact of unbalanced datasets on model performance. 【Findings】 Experi-ments were performed on a small heart failure dataset collected from Bethune Hospital for mor-tality prediction in heart failure patients within 30 days. The experimental results show that the accuracy and recall of the MK-SVM-AdaBoost model reach 85.63% and 86.33%, respectively, which are better than thase of the existing methods. The Area Under Curve (AUC) under the ROC curve enclosed with the axes and its micro-mean (MiA-AUC) reach 91.00% and 92.00%, respectively, which indicates that the proposed model has good stability. 【Conclusions】 The pro-posed model has high accuracy and stability, and can provide some reference for the clinical deci-sion-making of doctors. In the future, the dataset will be expaned and the graded warnings will be studied for more effective assessment of patients.

国家重大科研仪器研制项目(62027819);国家自然科学基金资助项目(62076177,61772358);山西省关键核心技术和共性技术研发专项资助项目(2020XXX007)

刘晓玉,李灯熬,赵菊敏.基于多核 SVM 的 AdaBoost心力衰竭死亡率评估模型[J].太原理工大学学报,2023,54(5):804-811.

LIU Xiaoyu,LI Dengao,ZHAO Jumin.A mortality predicting model for heart failure patients based on Ada Boost with multi-kernel SVM[J].Journal of Taiyuan University of Technology,2023,54(5):804-811.