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

Abstract

The surface electromyography (sEMG) is useful tool to diagnose of knee disorder in clinicalenvironments. It assists in designing the clinical decision support systems based classification.These systems exhibit complex structure because of sEMG data obtained at different postures atthis study. In this context, we have researched the classification performance of each postureusing artificial neural network (ANN) and logistic regression (LR) models and have showedthat 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 statisticalmethods. The results show that the proposed models can classify with over 95% of success, andalso the ANN model has higher performance than the LR model. Our ANN model outperformsreported studies in literature. The accuracy results indicate that the models used the only sittingposture data can exhibit successful classification for the knee disorder. Therefore, the usage ofcomplex dataset is prevented for diagnosing knee disorder.