Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

• Published in: Frontiers in neuroinformatics Vol. 16; p. 1029690
• Main Authors: Su, Hang, Han, Zhengyuan, Fu, Yujie, Zhao, Dong, Yu, Fanhua, Heidari, Ali Asghar, Zhang, Yu, Shou, Yeqi, 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; Asymptomatic; Blood coagulation; Blood pressure; Classification; Collaboration; Diagnosis; disease diagnosis; Embolism; Experiments; extreme learning machine; Feature selection; Genetic algorithms; Intelligence; Learning algorithms; Leukocytes (neutrophilic); Machine learning; Medical diagnosis; Medical research; meta-heuristic; Mortality; Neuroscience; Optimization algorithms; Optimization techniques; Parameter identification; Prediction models; Pulmonary arteries; Pulmonary artery; pulmonary embolism; Pulmonary embolisms; Support vector machines; Syncope; meta-heuristic; swarm-intelligence; extreme learning machine; disease diagnosis; machine learning; pulmonary embolism; feature selection
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2022.1029690

Pulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients. Combining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed. To confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital. The experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model's accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE.