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## Comparison of Artificial Neural Network and Regression Models to Diagnose of Knee Disorder in Different Postures Using Surface Electromyography

#### Rukiye Uzun [1] , Okan Erkaymaz [2] , Irem Senyer Yapici [3]

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The surface electromyography (sEMG) is useful tool to diagnose of knee disorder in clinical environments. It assists in designing the clinical decision support systems based classification. These systems exhibit complex structure because of sEMG data obtained at different postures at this study. In this context, we have researched the classification performance of each posture using artificial neural network (ANN) and logistic regression (LR) models and have showed that the classification success of the model used sitting posture data is higher than other postures (gait and standing). We have promoted this finding by using machine learning and statistical methods. The results show that the proposed models can classify with over 95% of success, and also the ANN model has higher performance than the LR model. Our ANN model outperforms reported studies in literature. The accuracy results indicate that the models used the only sitting posture data can exhibit successful classification for the knee disorder. Therefore, the usage of complex dataset is prevented for diagnosing knee disorder.

Artificial neural network, Computer aided diagnosis, Discrete wavelet transform, Surface electromyography
• Yalçın İşler, email: islerya@yahoo.com
• Umut Orhan, email:uorhan@cu.edu.tr
• Matjaz Perc, email:matjaz.perc@gmail.com
Konular Fen Computer Engineering Yazar: Rukiye UzunKurum: BÜLENT ECEVİT ÜNİVERSİTESİÜlke: Turkey Yazar: Okan ErkaymazKurum: BÜLENT ECEVİT ÜNİVERSİTESİÜlke: Turkey Yazar: Irem Senyer YapiciKurum: BÜLENT ECEVİT ÜNİVERSİTESİÜlke: Turkey
 Bibtex @araştırma makalesi { gujs335978, journal = {Gazi University Journal of Science}, issn = {}, eissn = {2147-1762}, address = {Gazi Üniversitesi}, year = {}, volume = {31}, pages = {100 - 110}, doi = {}, title = {Comparison of Artificial Neural Network and Regression Models to Diagnose of Knee Disorder in Different Postures Using Surface Electromyography}, key = {cite}, author = {Uzun, Rukiye and Erkaymaz, Okan and Senyer Yapici, Irem} } APA Uzun, R , Erkaymaz, O , Senyer Yapici, I . (). Comparison of Artificial Neural Network and Regression Models to Diagnose of Knee Disorder in Different Postures Using Surface Electromyography. Gazi University Journal of Science, 31 (1), 100-110. Retrieved from http://dergipark.gov.tr/gujs/issue/35772/335978 MLA Uzun, R , Erkaymaz, O , Senyer Yapici, I . "Comparison of Artificial Neural Network and Regression Models to Diagnose of Knee Disorder in Different Postures Using Surface Electromyography". Gazi University Journal of Science 31 (): 100-110 Chicago Uzun, R , Erkaymaz, O , Senyer Yapici, I . "Comparison of Artificial Neural Network and Regression Models to Diagnose of Knee Disorder in Different Postures Using Surface Electromyography". Gazi University Journal of Science 31 (): 100-110 RIS TY - JOUR T1 - Comparison of Artificial Neural Network and Regression Models to Diagnose of Knee Disorder in Different Postures Using Surface Electromyography AU - Rukiye Uzun , Okan Erkaymaz , Irem Senyer Yapici Y1 - 2019 PY - 2019 N1 - DO - T2 - Gazi University Journal of Science JF - Journal JO - JOR SP - 100 EP - 110 VL - 31 IS - 1 SN - -2147-1762 M3 - UR - Y2 - 2017 ER - EndNote %0 Gazi University Journal of Science Comparison of Artificial Neural Network and Regression Models to Diagnose of Knee Disorder in Different Postures Using Surface Electromyography %A Rukiye Uzun , Okan Erkaymaz , Irem Senyer Yapici %T Comparison of Artificial Neural Network and Regression Models to Diagnose of Knee Disorder in Different Postures Using Surface Electromyography %D 2019 %J Gazi University Journal of Science %P -2147-1762 %V 31 %N 1 %R %U ISNAD Uzun, Rukiye , Erkaymaz, Okan , Senyer Yapici, Irem . "Comparison of Artificial Neural Network and Regression Models to Diagnose of Knee Disorder in Different Postures Using Surface Electromyography". Gazi University Journal of Science 31 / 1 100-110.