Due to the large size of this data set, only four sections were included in this study, i.e. One hundred samples of n pairs of numbers were generated and the performance of models was assessed for samples of size 15, 30, and 60. Learning process in ANN models is accomplished through special training algorithms that are developed to mimic the learning mechanism of biological systems (Cheng and Titterington, 1994; Johnson and Wichern, 1998; Haykin, 1999; Zilouchian, 2001; Hajmeer and Basheer, 2003). The input and hidden nodes' weights will be adjusted based on the error between the estimated output and actual output () during the training phase. A supplementary section was added to the Work Compatibility questionnaire in order to collect demographic information as well as work-related disorders and diseases from all subjects, that is, the frequency and severity of pain in major body joints (neck, shoulders, elbows/forearms, hands/wrists, fingers, upper back, lower back, hips/thighs, knees/lower legs, and ankles/feet) and clinical symptoms of illnesses, i.e.
Further studies are required to investigate more complex and larger networks. And finally, the distribution of jobs of the sample is presented in Table 5. The average height and weight of the sample were 179.8 cm and 87.42 kg, with standard deviation of 6.49 cm and 15.97 kg, respectively. ANN models require to be trained with acceptable accuracy before being used for data analysis. Because of the large size of the initial dataset and limited resources available for high-volume computation and data analysis, the dataset was divided into 160 subsets based on outcome variables and for each subset, separate LR and ANN models were developed, by which their performances were assessed and compared. Each section is made of different number of questions (total of 166 questions), which were subjectively answered by participating workers on a Likert scale of 5 (from ‘not at all’ to ‘entirely’). Based on the characteristics of ROC curve, the area under the curve is often taken as a scalar measure of the model's overall performance. Although this typically simplifies data analysis and Both methods have the three basic entities for system identification, i.e. Please log in from an authenticated institution or log into your member profile to access the email feature. Tables 2–4 represent the age distribution of the sample, the years of experience of subjects in construction, and the ethnicity distribution of the sample, respectively. This page was last edited on 11 March 2020, at 16:22. None of the authors of these articles attempted to estimate the parameters of the statistical model or predict the causal effect of input variables on outputs. The lack of homogeneousness in the data set used in this research, choice of training algorithm, and ANN model structure are considered to be the potential reasons that 33 of LR models outperformed the ANN models. The approach described by Hosmer and Lemeshow (2000) was used as the general guideline for selecting variables that result in a ‘best’ model within the context of this research.
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