Sayin, RefahKeskin, SiddikHamamci, Mehmet2025-05-102025-05-1020170030-99822-s2.0-85031014331https://hdl.handle.net/20.500.14720/17770Hamamci, Mehmet/0000-0001-7100-3952Objective: To investigate the performance and effectiveness of 4 classification methods including support vector machine, naive Bayes, classification tree, and artificial neural network in the detection of carpal tunnel syndrome. Methods: This retrospective study was conducted at Yuzuncu Yil University, Van, Turkey, and comprised record of patients suspected of having carpal tunnel syndrome between January and December 2013. The evaluations included age, gender, and 6 electromyography variables, including right/left median nerve sensory velocity, right/left fourth finger peak latency difference, and right/left median nerve motor distal latency. We investigated the performance of classification methods such as support vector machine, naive Bayes, classification tree and artificial neural network in the patients using data obtained from electromyography scan. A total of 6 criteria were used for the assessment of performance, including: true positive rate, false positive rate, true negative rate, false negative rate, accuracy, and preciseness. Results: Of the 109 patients, 88(80.7%) were women and 21(19.3%) men. Besides, 67(61.5%) participants had carpal tunnel syndrome and 42(38.5%) did not have it. On classification tree, only 2 variables, i.e. left fourth finger peak latency difference and right/left median nerve sensory velocity, were found to be statistically significant (p<0.001). Naive Bayes had the highest detection score (91.04%), followed by support vector machine (89.55%). Conclusion: Naive Bayes yielded better performance than all the other methods in the diagnosis of carpal tunnel syndrome, followed by support vector machine.eninfo:eu-repo/semantics/closedAccessCarpal Tunnel SyndromeEntrapment NeuropathyElectromyographyNaive BayesArticle6711Q4Q31654165729171554WOS:000418462000004