A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China

• Published in: Frontiers in neuroinformatics Vol. 16; p. 1052868
• Main Authors: Su, Hang, Shou, Yeqi, Fu, Yujie, Zhao, Dong, Heidari, Ali Asghar, Han, Zhengyuan, Wu, Peiliang, Chen, Huiling, Chen, Yanfan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 16.12.2022; Frontiers Media S.A
• Subjects: Accuracy; Adaptability; Algorithms; Blood pressure; Classification; Disease; disease diagnosis; Embolism; extreme learning machine; Feature selection; Intelligence; Learning algorithms; Machine learning; Medical research; Mortality; Neural networks; Neuroscience; Optimization algorithms; Patients; Prediction models; Pulmonary arteries; pulmonary embolism; Pulmonary embolisms; Risk assessment; Statistical methods; Support vector machines; swarm intelligence; Syncope; Thromboembolism; Tumors; disease diagnosis; pulmonary embolism; extreme learning machine; feature selection; swarm intelligence
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2022.1052868

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.