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Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model
被引：0
作者：
Zhang L.
[1
]
Yu R.
[2
]
Chen K.
[1
]
Zhang Y.
[3
,4
]
Li Q.
[5
]
Chen Y.
[6
]
机构：
[1] The First Clinical College, Wenzhou Medical University, Wenzhou
[2] Cardiac Care Unit, Sir RUN RUN Shaw Hospital, Hangzhou
[3] Wenzhou Medical University School of Nursing, 325000, Wenzhou
[4] Cixi Biomedical Research Institute, Wenzhou Medical University, Cixi
[5] School of Computer Science and Technology, Beijing Institute of Technology, Beijing
[6] Nursing Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou
关键词：
Coronavirus disease 2019;
Deep vein thrombosis;
Fuzzy K-nearest neighbor;
Runge Kutta optimizer;
D O I：
10.1016/j.compbiomed.2024.108294
中图分类号：
学科分类号：
摘要：
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. © 2024 Elsevier Ltd
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