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
来源：
Computers in Biology and Medicine | 2024年 / 173卷
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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共 81 条
  • [11] Rasappan P., Et al., Transforming sentiment analysis for e-commerce product reviews: hybrid deep learning model with an innovative term weighting and feature selection, Inf. Process. Manag., 61, 3, (2024)
  • [12] Manikandan G., Et al., Classification models combined with Boruta feature selection for heart disease prediction, Inform. Med. Unlocked, 44, (2024)
  • [13] Zhou X., Et al., Random following ant colony optimization: continuous and binary variants for global optimization and feature selection, Appl. Soft Comput., 144, (2023)
  • [14] Kamulegeya L., Et al., Using artificial intelligence on dermatology conditions in Uganda: a case for diversity in training data sets for machine learning, Afr. Health Sci., 23, 2, pp. 753-763, (2023)
  • [15] Zhu X., Et al., Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction, Clin. Chim. Acta, (2024)
  • [16] Nielsen H.K., Pathophysiology of venous thromboembolism, Semin. Thromb. Hemost., 17, pp. 250-253, (1991)
  • [17] Storn R., Price K., Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11, 4, pp. 341-359, (1997)
  • [18] Kennedy J., Eberhart R., Particle swarm optimization, ICNN'95-International Conference on Neural Networks, (1995)
  • [19] Chen H., Et al., Slime mould algorithm: a comprehensive review of recent variants and applications, Int. J. Syst. Sci., pp. 1-32, (2022)
  • [20] Li S., Et al., Slime mould algorithm: a new method for stochastic optimization, Future Generat. Comput. Syst., 111, pp. 300-323, (2020)
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