Please use this identifier to cite or link to this item: 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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