Development of Neural Network Model to Classify Women Carrying Water Jobs in Terms of Physiological Risk

Abstract

carrying water generally considered to be a profession with high level of physical stress that tend to increase and lead to a high risk of MSDs. So, the objective of this study was to develop an Artificial Neural Network (ANN) based diagnostic model which can classify water carrying jobs according to the potential risk of physiological stress. ANN models method of predicting outcomes, which are gradually finding their way into the safety field. Limited studies have shown that they are capable of predicting outcomes more accurately than statistical analysis. Back Propagation algorithm with LM training was used in this study to train the ANN architecture and same has been tested for the various data sets. The data sets used in this research was collected from the physiological study of WCW. This research work demonstrates that the developed ANN model exhibits good performance in prediction of physiological stress in high and low risk categories. This model provides a higher proportion of correct classification than other previous models. So, the system can be useful in injury prevention due to manual carrying of loads.