## 4 Results
### Model Intepretation and Application in Safety  
Based on the analysis in this study, both MLP and RBF NN models show high performance in classifying and predicting multi-categorical incident causes in workers' compensation claim data in occupational injuries from within commercial grain elevators. The results show that applying ANNs could be a straightforward approach for probabilistic prediction of the main causes of incidents based on prior injuries data (type and nature), as well as on the workers' age, experience, and occupation. 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; 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. However, each category has many details, from amputation and laceration, to contusion, fracture, hearing impairment, and vision loss. For example, regardless of occupation or injury type, the cause of carpal tunnel syndrome in all occupational injuries was most likely _strain_ (0.75-0.79 probability). The main cause of incidents of inflammation was predicted to be _struck or injured by_, with little variation in the prediction probability based on occupation class (0.35-0.46 probability). Dislocation injuries have a probability of between 0.60 to 0.79 to occur due to the _fall_, _slip_, _or trip_ cause group. The results also indicate the significant effect of the workers' occupation in predicting the potential causes of the incidents, which agrees with previous studies on the significance of work-related activities in determining the probability of specific safety risks and incident severity in agro-industry workers [65; 66; 67]. Although the experience and age of the worker are not the most significant contributors to the probability of a specific safety risk from the RBF model, they both show a high impact on the sensitivity analysis of the MLP model. This also agrees with the previous literature about the importance of workers' age and their years on the job in the occurrence of a specific incident [68; 69; 70]. The main application of this model could be to provide a probabilistic table of various scenarios, for which the main cause of incident can be estimated [39]. A variety of levels for each predictor of the multi-categorical cause group output variable were fed into the ANNs model, and the probability of each cause group incidence was calculated and compared. The cause group with the highest probability was then classified as the main future cause of injury. For example, a puncture medical injury in a younger worker, with less experience, was mostly estimated to occur for a fall, slip, or trip, while the same puncture in an older worker, with more years on the job, was estimated to be caused by being struck, and has a permanent partial disability outcome. Table 8 shows various scenarios and their predicted cause group, based on the ANNs model results for a worker occupied in grain elevator operations with a permanent partial disability injury.