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Title:: Classification and diagnosis of chest pain triad based on Cuckoo search optimized support vector machine

Author(s):: ZHAO Yifan1; BIAN Liang2; ZHANG Feifei1; 1.School of Public Health and Management, Ningxia Medical University, Yinchuan 750000，China;2.School of Science,Ningxia Medical University, Yinchuan 750000

Keywords:: Cuckoo search; Support vector machine; Chest pain triad; Imbalanced data; Dissection of aorta; Pulmonary embolism; Acute myocardial infarction

摘要:: 胸痛三联征在临床上有相似的胸痛症状，误诊率居高，其确切病因尚不十分明确。针对经典支持向量机不适用于胸痛三联征此类非平衡数据集分类的缺点，本研究结合径向基核函数、布谷鸟算法以及支持向量机，提出一种基于布谷鸟算法优化支持向量机的分类识别模型，用于胸痛三联征的分类诊断。在收集到的735例有效样本数据集上，采用Java程序抽取平衡数据集。实验结果显示，基于平衡数据集，该模型的平均正确率为80.667%；基于非平衡数据集，其平均正确率为97.767%，相比经典支持向量机、粒子群算法-支持向量机、遗传算法-支持向量机均有不同程度的提高。因此，本研究模型对胸痛三联征的分类诊断具有一定的参考价值。

Abstract:: The triad of chest pain has similar symptoms of chest pain in the clinic, the rate of misdiagnosis is high. The exact cause is not very clear. In view of the disadvantage that classical support vector machine is not suitable for the classification of unbalanced data sets such as chest pain triad, we proposed a classification based on Cuckoo algorithm to optimize support vector machine based on radial basis kernel function, Cuckoo algorithm and support vector machine. The identification model proposed could be used for the classification diagnosis of triad of chest pain. In the collected 735 valid sample data sets, the balanced data set was extracted by Java program. The experimental results showed that the average accuracy of the model was 80.667% based on the balanced data set and 97.767% based on the imbalanced data set, which was higher than that of the classical support vector machine(SVM),particle swarm optimization-SVM(PSO-SVM), genetic algorithm SVM(GA-SVM). Therefore, this model has certain reference value for the classification diagnosis of triad of chest pain.