Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model
Copyright © 2024 Elsevier Ltd. All rights reserved..
BACKGROUND: Deep vein thrombosis (DVT) is a significant complication in coronavirus disease 2019 patients, arising from coagulation issues in the deep venous system. Among 424 scheduled patients, 202 developed DVT (47.64%). DVT increases hospitalization risk, and complications, and impacts prognosis. Accurate prognostication and timely intervention are crucial to prevent DVT progression and improve patient outcomes.
METHODS: This study introduces an effective DVT prediction model, named bSES-AC-RUN-FKNN, which integrates fuzzy k-nearest neighbor (FKNN) with enhanced Runge-Kutta optimizer (RUN). Recognizing the insufficient effectiveness of RUN in local search capability and its convergence accuracy, spherical evolutionary search (SES) and differential evolution-inspired knowledge adaptive crossover (AC) are incorporated, termed SES-AC-RUN, to enhance its optimization capability.
RESULTS: Based on the benchmark set by CEC 2017 and comparative analyses with several peers, it is evident that SES-AC-RUN significantly enhances search performance compared to traditional RUN, even standing comparably against leading championship algorithms. The proposed bSES-AC-RUN-FKNN model was applied to predict a dataset comprising 424 cases of DVT patients, totaling 7208 records. Remarkably, the model demonstrates outstanding accuracy, reaching 91.02%, alongside commendable sensitivity at 91.07%.
CONCLUSIONS: The bSES-AC-RUN-FKNN emerges as a robust and efficient predictive tool, significantly enhancing the accuracy of DVT prediction. This model can be used to manage the risk of thrombosis in the care of COVID-19 patients. Nursing staff can combine the model's predictions with clinical judgment to formulate comprehensive treatment approaches.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:173 |
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| Enthalten in: |
Computers in biology and medicine - 173(2024) vom: 17. Apr., Seite 108294 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Lufang [VerfasserIn] |
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| Links: |
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| Themen: |
Coronavirus disease 2019 |
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| Anmerkungen: |
Date Completed 17.04.2024 Date Revised 17.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2024.108294 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a BACKGROUND: Deep vein thrombosis (DVT) is a significant complication in coronavirus disease 2019 patients, arising from coagulation issues in the deep venous system. Among 424 scheduled patients, 202 developed DVT (47.64%). DVT increases hospitalization risk, and complications, and impacts prognosis. Accurate prognostication and timely intervention are crucial to prevent DVT progression and improve patient outcomes | ||
| 520 | |a METHODS: This study introduces an effective DVT prediction model, named bSES-AC-RUN-FKNN, which integrates fuzzy k-nearest neighbor (FKNN) with enhanced Runge-Kutta optimizer (RUN). Recognizing the insufficient effectiveness of RUN in local search capability and its convergence accuracy, spherical evolutionary search (SES) and differential evolution-inspired knowledge adaptive crossover (AC) are incorporated, termed SES-AC-RUN, to enhance its optimization capability | ||
| 520 | |a RESULTS: Based on the benchmark set by CEC 2017 and comparative analyses with several peers, it is evident that SES-AC-RUN significantly enhances search performance compared to traditional RUN, even standing comparably against leading championship algorithms. The proposed bSES-AC-RUN-FKNN model was applied to predict a dataset comprising 424 cases of DVT patients, totaling 7208 records. Remarkably, the model demonstrates outstanding accuracy, reaching 91.02%, alongside commendable sensitivity at 91.07% | ||
| 520 | |a CONCLUSIONS: The bSES-AC-RUN-FKNN emerges as a robust and efficient predictive tool, significantly enhancing the accuracy of DVT prediction. This model can be used to manage the risk of thrombosis in the care of COVID-19 patients. Nursing staff can combine the model's predictions with clinical judgment to formulate comprehensive treatment approaches | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Coronavirus disease 2019 | |
| 650 | 4 | |a Deep vein thrombosis | |
| 650 | 4 | |a Fuzzy K-nearest neighbor | |
| 650 | 4 | |a Runge Kutta optimizer | |
| 700 | 1 | |a Yu, Renyue |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Keya |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Qiang |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yu |e verfasserin |4 aut | |
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