A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China
IntroductionPulmonary 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.MethodsUsing the clinical data from the First Affil...
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| Format: | Article |
| Language: | English |
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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.2022.1052868/full |
| _version_ | 1797980768115359744 |
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| author | Hang Su Yeqi Shou Yujie Fu Dong Zhao Ali Asghar Heidari Zhengyuan Han Peiliang Wu Huiling Chen Yanfan Chen |
| author_facet | Hang Su Yeqi Shou Yujie Fu Dong Zhao Ali Asghar Heidari Zhengyuan Han Peiliang Wu Huiling Chen Yanfan Chen |
| author_sort | Hang Su |
| collection | DOAJ |
| description | IntroductionPulmonary 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.MethodsUsing 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.ResultsIn 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.DiscussionThe 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. |
| first_indexed | 2024-04-11T05:59:00Z |
| format | Article |
| id | doaj.art-be77365a84c24886ab7dc000006bec07 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-04-11T05:59:00Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-be77365a84c24886ab7dc000006bec072022-12-22T04:41:48ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962022-12-011610.3389/fninf.2022.10528681052868A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, ChinaHang Su0Yeqi Shou1Yujie Fu2Dong Zhao3Ali Asghar Heidari4Zhengyuan Han5Peiliang Wu6Huiling Chen7Yanfan Chen8College 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, ChinaSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranDepartment 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 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.MethodsUsing 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.ResultsIn 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.DiscussionThe 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.https://www.frontiersin.org/articles/10.3389/fninf.2022.1052868/fullfeature selectionextreme learning machinedisease diagnosisswarm intelligencepulmonary embolism |
| spellingShingle | Hang Su Yeqi Shou Yujie Fu Dong Zhao Ali Asghar Heidari Zhengyuan Han Peiliang Wu Huiling Chen Yanfan Chen A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China Frontiers in Neuroinformatics feature selection extreme learning machine disease diagnosis swarm intelligence pulmonary embolism |
| title | A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China |
| title_full | A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China |
| title_fullStr | A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China |
| title_full_unstemmed | A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China |
| title_short | A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China |
| title_sort | new machine learning model for predicting severity prognosis in patients with pulmonary embolism study protocol from wenzhou china |
| topic | feature selection extreme learning machine disease diagnosis swarm intelligence pulmonary embolism |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2022.1052868/full |
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