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http://hdl.handle.net/20.500.11889/8329
Title: | Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis | Authors: | Xia, Jianfu Wang, Zhifei Yang, Daqing Li, Rizeng Liang, Guoxi Chen, Huiling Heidari, Ali Asghar Turabieh, Hamza Mafarja, Majdi Pan, Zhifang |
Keywords: | Machine learning;Support vector machine;Mathematical statistics;Feature selection;Grasshopper optimization algorithm;Swarm intelligence;Evolutionary computation;Deep learning (Machine learning);Appendicitis - Diagnosis;Opposition-based learning | Issue Date: | 2022 | Publisher: | Computers in Biology and Medicine | Abstract: | Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study. First, the most significant variables, including C-reactive protein (CRP), heart rate, body temperature, and neutrophils discriminating complicated from uncomplicated appendicitis, were identified using random forest analysis. Second, an improved grasshopper optimization algorithm-based support vector machine was used to construct the diagnostic model to discriminate complicated appendicitis (CAP) from uncomplicated appendicitis (UAP). The resultant optimal model can produce an average of 83.56% accuracy, 81.71% sensitivity, 85.33% specificity, and 0.6732 Matthews correlation coefficients. Based on existing routinely available markers, the proposed intelligent diagnosis model is highly reliable. Thus, the model can potentially be used to assist doctors in making correct clinical decisions. | URI: | http://hdl.handle.net/20.500.11889/8329 | DOI: | 10.1016/j.compbiomed.2021.105206 |
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Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis.pdf | 2.39 MB | Adobe PDF | View/Open |
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