Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations

Kakhki, Fatemeh Davoudi; Freeman, Steven A.; Mosher, Gretchen A.

doi:10.3390/app9173449

Cited by 14 publications

(7 citation statements)

References 43 publications

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“…The results from the analysis emphasize the role of the nature of injury in such predictions and classifications, and are in agreement with the previous literature on the significance of the nature of injury in determining the severity of occupational injuries [15,16,[59][60][61][62][63][64]. In general, injuries are divided into three main groups in workers' compensation claims: occupational disease, multiple injuries, and specific injuries.…”

Section: Model Intepretation and Application In Safetysupporting

confidence: 86%

“…The expansion of grain storage capacity in recent years has led to a higher number of occupational incidents in grain handling industrial operations [14]. The frequency and costs of occupational incidents in grain elevators are higher than in other agribusinesses [11,[15][16][17]. Few studies have addressed safety issues in the grain handling industry.…”

Section: Introductionmentioning

confidence: 99%

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Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019

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The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

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Section: Model Intepretation and Application In Safetysupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019

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“…However, logistic regression is useful when working with a linearly separable target class [ 27 ]. Logistic regression has been widely used in studies related to occupational safety [ 5 , 14 , 15 , 16 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Statistical and machine learning techniques have been commonly used to analyze the importance of contributing factors toward an accident in the domain of occupation safety [ 10 , 11 , 12 , 13 , 14 ]. Classification trees, support vector machines, and neural networks are the most widely used machine learning models in this domain.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations

Yedla

Kakhki

Jannesari

2020

IJERPH

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Mining is known to be one of the most hazardous occupations in the world. Many serious accidents have occurred worldwide over the years in mining. Although there have been efforts to create a safer work environment for miners, the number of accidents occurring at the mining sites is still significant. Machine learning techniques and predictive analytics are becoming one of the leading resources to create safer work environments in the manufacturing and construction industries. These techniques are leveraged to generate actionable insights to improve decision-making. A large amount of mining safety-related data are available, and machine learning algorithms can be used to analyze the data. The use of machine learning techniques can significantly benefit the mining industry. Decision tree, random forest, and artificial neural networks were implemented to analyze the outcomes of mining accidents. These machine learning models were also used to predict days away from work. An accidents dataset provided by the Mine Safety and Health Administration was used to train the models. The models were trained separately on tabular data and narratives. The use of a synthetic data augmentation technique using word embedding was also investigated to tackle the data imbalance problem. Performance of all the models was compared with the performance of the traditional logistic regression model. The results show that models trained on narratives performed better than the models trained on structured/tabular data in predicting the outcome of the accident. The higher predictive power of the models trained on narratives led to the conclusion that the narratives have additional information relevant to the outcome of injury compared to the tabular entries. The models trained on tabular data had a lower mean squared error compared to the models trained on narratives while predicting the days away from work. The results highlight the importance of predictors, like shift start time, accident time, and mining experience in predicting the days away from work. It was found that the F1 score of all the underrepresented classes except one improved after the use of the data augmentation technique. This approach gave greater insight into the factors influencing the outcome of the accident and days away from work.

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“…Occupational and work-related accidents were addressed by Shafique and Rafiq, "An Overview of Construction Occupational Accidents in the Hong Kong: A Recent Trend and Future Perspectives" [4], whereas Kakhki and co-workers discussed the "Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations" [5]. Also on the civil construction domain, Derlukiewicz discussed the "Application of a Design and Construction Method Based on a Study of User Needs In The Prevention of Accidents Involving Operators of Demolition Robots" [6].…”

Section: Special Issue Papersmentioning

confidence: 99%

Special Issue: Accident Analysis and Prevention: Experimental and Numerical Approaches

Sousa

Ptak

2020

Applied Sciences

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Use of Logistic Regression to Identify Factors Influencing the Post-Incident State of Occupational Injuries in Agribusiness Operations

Cited by 14 publications

References 43 publications

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations

Special Issue: Accident Analysis and Prevention: Experimental and Numerical Approaches

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