ARTIFICIAL INTELLIGENCE BASED RATING OF CARPAL TUNNEL SYNDROME EFFICACY IN CLINICAL DIAGNOSIS

Authors : Elif SARICA DAROL, Yıldız ECE, Süleyman UZUN, Murat ALEMDAR

Pages : 213-219

View : 37 | Download : 29

Publication Date : 2023-06-30

Article Type : Research Paper

Abstract :Objective: The most common entrapment neuropathy seen by the clinician is Carpal tunnel syndrome insert ignore into journalissuearticles values(CTS);. CTS is graded as mild, moderate, and severe according to the results obtained on electroneuromyography insert ignore into journalissuearticles values(ENMG); by clinicians. We aimed to show the effectiveness of the use of artificial intelligence in clinical diagnosis in the grading of CTS. Methods: In our study, the data of 315 people with a pre-diagnosis of CTS were used and classified into four classes based on AI as CTS grade. Machine Learning insert ignore into journalissuearticles values(ML); algorithms Ensemble, Support Vector Machine insert ignore into journalissuearticles values(SVM);, K-Nearest Neighbor insert ignore into journalissuearticles values(KNN);, and Decision Tree insert ignore into journalissuearticles values(Tree); algorithms were used in classification processes. 10% Hold-out validation was used and the learning rate was determined as 0.1. As a result of the classification, accuracy, precision, sensitivity, specificity, and F1-score performance values were obtained. Results: SVM made the best estimation and KNN made the worst estimation in the 0 class. The best estimate in class 1 belongs to the Support Vector Machine. Ensemble and Tree made the best guesses in the 2nd and 3rd grades. In our study, the best algorithm with an overall success rate is SVM with 93.55%. Conclusions: The results showed that ML algorithm models consistently provided better predictive results and would assist physicians in determining the medical treatment modality of CTS. Artificial intelligence insert ignore into journalissuearticles values(AI); techniques are reliable methods that assist clinicians to deliver quality healthcare.
Keywords : karpal tünel sendromu, elektromiyografi, yapay zeka, derecelendirme