Showing 2 results for Prediction
Sajjad Samiei, Mahsa Alefi, Zahra Alaei, Reza Pourbabaki,
Volume 3, Issue 2 (4-2019)
Abstract
Background: Musculoskeletal disorders are one of the most common factors that lead to occupational injuries among hospital staff. Considering the key role of hospital staffs in providing health services to patients, this study was conducted to assess risk factors that are effective on low back pain and the use of adaptive neuro-fuzzy inference system (ANFIS) model to predict it. Methods: This cross-sectional study was conducted in 90 nurses of the Isfahan hospitals in 2018. First, the risk factors that affect pain in the lumbar region was assessed, then a model with the precision of 0.91% to predict low back pain was developed using the ANFIS by the MATLAB2016a software. Results: First, linear regression model showed four risk factors repetitive movements, long-standing, bending of the back, and carrying heavy objects were the most significant ones compared to other risk factors associated with musculoskeletal disorders. After a study of these risk factors in the ANFIS, various tests were conducted and the best model with a confidence level of 91% was selected as the model. Conclusion: The ANFIS can be used as an appropriate tool to predict lower back pain.
Reza Pourbabaki, Zahra Beigzadeh, Behnam Haghshenas, Ali Karimi, Miss Zahra Alaei, Saeid Yazdanirad,
Volume 4, Issue 2 (4-2020)
Abstract
Background: Unsafe behavior in industries can be due to different factors. The aim of this study was to predict and model unsafe behavior using a safety atmosphere and cultural attitudes questionnaires. Methods: This study was a descriptive-analytic and cross-sectional examination that analyzed the data and predicted the unsafe behaviors of 90 construction workers using Neuro-Fuzzy Inference System (ANFIS) in MATLAB R2016a software. Results: In this study, the model of the safety atmosphere - unsafe behavior and the model of the cultural attitudes - unsafe behavior had the regression coefficients of 0.93373 and 0.9234, respectively. It showed that each of the parameters has a close relationship to the rate of the unsafe behavior. In this regard, a combination of the safety atmosphere and safety attitude parameters for the estimation of the unsafe behaviors achieved the better results with a regression coefficient of 0.9453 which indicates the direct effect of both parameters simultaneously on unsafe behavior. Conclusion: Based on the findings, it can be concluded that the neuro-fuzzy model can be used as an appropriate tool for predicting unsafe behavior in the industries.
