Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques
IntroductionPulmonary 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 b...
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| Format: | Article |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2022.1029690/full |
| _version_ | 1828266586527498240 |
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| author | Hang Su Zhengyuan Han Yujie Fu Dong Zhao Fanhua Yu Ali Asghar Heidari Yu Zhang Yeqi Shou Peiliang Wu Huiling Chen Yanfan Chen |
| author_facet | Hang Su Zhengyuan Han Yujie Fu Dong Zhao Fanhua Yu Ali Asghar Heidari Yu Zhang Yeqi Shou Peiliang Wu Huiling Chen Yanfan Chen |
| author_sort | Hang Su |
| collection | DOAJ |
| description | IntroductionPulmonary 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.MethodsCombining 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.ResultsTo 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.DiscussionThe 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. |
| first_indexed | 2024-04-13T04:55:04Z |
| format | Article |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-04-13T04:55:04Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-2af9819710994affa843ea348327f7c02022-12-22T03:01:31ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962022-12-011610.3389/fninf.2022.10296901029690Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniquesHang Su0Zhengyuan Han1Yujie Fu2Dong Zhao3Fanhua Yu4Ali Asghar Heidari5Yu Zhang6Yeqi Shou7Peiliang Wu8Huiling Chen9Yanfan Chen10College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, ChinaDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaCollege of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, ChinaCollege of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, ChinaSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranCollege of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, ChinaDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, ChinaDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaIntroductionPulmonary 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.MethodsCombining 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.ResultsTo 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.DiscussionThe 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.https://www.frontiersin.org/articles/10.3389/fninf.2022.1029690/fullpulmonary embolismfeature selectionextreme learning machinedisease diagnosismachine learningmeta-heuristic |
| spellingShingle | Hang Su Zhengyuan Han Yujie Fu Dong Zhao Fanhua Yu Ali Asghar Heidari Yu Zhang Yeqi Shou Peiliang Wu Huiling Chen Yanfan Chen Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques Frontiers in Neuroinformatics pulmonary embolism feature selection extreme learning machine disease diagnosis machine learning meta-heuristic |
| title | Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques |
| title_full | Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques |
| title_fullStr | Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques |
| title_full_unstemmed | Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques |
| title_short | Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques |
| title_sort | detection of pulmonary embolism severity using clinical characteristics hematological indices and machine learning techniques |
| topic | pulmonary embolism feature selection extreme learning machine disease diagnosis machine learning meta-heuristic |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2022.1029690/full |
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