Su Hang, Shou Yeqi, Fu Yujie, Zhao Dong, Heidari Ali Asghar, Han Zhengyuan, Wu Peiliang, Chen Huiling, Chen Yanfan
College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, China.
Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Front Neuroinform. 2022 Dec 16;16:1052868. doi: 10.3389/fninf.2022.1052868. eCollection 2022.
Pulmonary embolism (PE) is a common thrombotic disease and potentially deadly cardiovascular disorder. The ratio of clinical misdiagnosis and missed diagnosis of PE is very large because patients with PE are asymptomatic or non-specific.
Using the clinical data from the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China), we proposed a swarm intelligence algorithm-based kernel extreme learning machine model (SSACS-KELM) to recognize and discriminate the severity of the PE by patient's basic information and serum biomarkers. First, an enhanced method (SSACS) is presented by combining the salp swarm algorithm (SSA) with the cuckoo search (CS). Then, the SSACS algorithm is introduced into the KELM classifier to propose the SSACS-KELM model to improve the accuracy and stability of the traditional classifier.
In the experiments, the benchmark optimization performance of SSACS is confirmed by comparing SSACS with five original classical methods and five high-performance improved algorithms through benchmark function experiments. Then, the overall adaptability and accuracy of the SSACS-KELM model are tested using eight public data sets. Further, to highlight the superiority of SSACS-KELM on PE datasets, this paper conducts comparison experiments with other classical classifiers, swarm intelligence algorithms, and feature selection approaches.
The experimental results show that high D-dimer concentration, hypoalbuminemia, and other indicators are important for the diagnosis of PE. The classification results showed that the accuracy of the prediction model was 99.33%. It is expected to be a new and accurate method to distinguish the severity of PE.
肺栓塞（PE）是一种常见的血栓性疾病，也是具有潜在致命性的心血管疾病。由于肺栓塞患者可能无症状或症状不具有特异性，其临床误诊和漏诊率非常高。
利用温州医科大学附属第一医院（中国温州）的临床数据，我们提出了一种基于群体智能算法的核极限学习机模型（SSACS-KELM），通过患者的基本信息和血清生物标志物来识别和区分肺栓塞的严重程度。首先，通过将樽海鞘群算法（SSA）与布谷鸟搜索（CS）相结合，提出了一种增强方法（SSACS）。然后，将SSACS算法引入KELM分类器，提出SSACS-KELM模型，以提高传统分类器的准确性和稳定性。
在实验中，通过基准函数实验将SSACS与五种原始经典方法和五种高性能改进算法进行比较，证实了SSACS的基准优化性能。然后，使用八个公共数据集测试了SSACS-KELM模型的整体适应性和准确性。此外，为了突出SSACS-KELM在肺栓塞数据集上的优越性，本文与其他经典分类器、群体智能算法和特征选择方法进行了比较实验。
实验结果表明，高D-二聚体浓度、低白蛋白血症等指标对肺栓塞的诊断具有重要意义。分类结果显示，预测模型的准确率为99.33%。有望成为一种区分肺栓塞严重程度的新的准确方法。
