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- W3127096198 abstract "Response surface model (RSM) is used to detect the variable values that make the response variable maximum or minimum. Besides, the effect of exploratory variables on the response variable is determined. Thus, this method can be referred as a combination of regression analysis and optimization. RSM is mostly used in many fields such as industry and chemistry. However, it has limited application in the field of health. The upper limb performance assessment is a two-stage assessment of upper limb contributions to task performance. In this study, the upper limb performance of chronic neck pain patients is examined on 63 patients. The upper extremity functional index (UEFI-20) identifying the performance of upper limb is assigned as response variable. Input variables are taken as the variables related the pain-rating scales of patients at rest or in activity. The central composite model is implemented to estimate the model. The artificial neural network (ANN) approach is also applied to upper limb performance data. The mean absolute error, correlation coefficients, standard error of prediction are obtained from evaluating the experimental and predicted values of both models. The comparative analysis for both models is made on the prediction accuracy." @default.
- W3127096198 created "2021-02-15" @default.
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- W3127096198 date "2021-01-30" @default.
- W3127096198 modified "2023-10-14" @default.
- W3127096198 title "The Comparison of Artificial Neural Network Approach and Response Surface Model for Evaluation Upper Limb Performance in Patients with Chronic Neck Pain" @default.
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- W3127096198 doi "https://doi.org/10.7240/jeps.748256" @default.
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