## 4 Results
### Model Performance  
Table 3 depicts the results for the classification and prediction performance for both MLP and RBF models. The overall model accuracy rates in all data sets was over 60%. Deciding on the test dataset for model performance, the ANNs model was able to accurately classify and predict the causes of incidents (no matter what group) in 64% of cases. Tables 4 and 5 show the MLP and RBF models performance in the prediction and classification of individual safety risk sources (cause group). From the MLP model performance on the test sets, the incident cause groups with the highest prediction accuracy were _cut_, _puncture_, _scrape_ (86.1%), _strain or injury by_ (84.4%), and _heat or cold exposure_ (75.7%). The MLP ANNs predicted that _cut_, _puncture_, _scrape_ would have the highest chance of being the main cause of incident occurrence in grain elevator occupational injuries, followed by _strain_, _and heat or cold exposure_.  
The other cause groups had a relatively lower prediction and classification rate. Incident cause group of _fall_, _slip_, _or trip injury_ had a 50.4% classification accuracy rate. This means that future incidents in grain elevator operations were expected to happen due to falls, slips, or trips with a probability of almost 0.51. _Struck or injured by_, and _caught in_, _under_, _or between_ injury groups had the lowest accuracy rate: 28.9%, and 5.0% respectively. As suggested by the literature, in general, binary categorical outputs are expected to have a higher prediction accuracy (random performance: 50%), compared to three-level categorical outputs (random performance: 33.3%) [54]. As categories increased, the expected accuracy per class dropped [55]. This explains the lower classification accuracy in specific cause groups (_caught in_, _under_, _or between_; _struck or injured by_).  
The results from the RBF model agrees with the MLP outcomes, with a bit less accuracy per predicted class.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Criteria** & **MLP** & **RBF** \\ \hline \multirow{4}{*}{Train} & Sum of Squares Error & N/A & 1161.041 \\  & Cross Entropy Error & 3867.614 & N/A \\  & Percent Incorrect Predictions & 35.2\% & 39.4\% \\  & Overall Accuracy & 64.8\% & 60.6\% \\  & Sum of Squares Error & N/A & 236.173 \\  & Cross Entropy Error & 819.830 & N/A \\  & Percent Incorrect Predictions & 38.6\% & 40.5\% \\  & Overall Accuracy & 61.4\% & 59.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Predictive ANNs model confusion rate and area under curve (AUC) per cause group.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Classification**} \\ \hline \multirow{4}{*}{**Sample**} & \multirow{4}{*}{**Observed**} & \multicolumn{6}{c}{**Predicted**} \\ \cline{3-10}  & & **Caught In,** & **Cut,** & **Fall, Slip,** & **Heat or** & **Strain or** & **Strack or** & **Percent** \\  & & **Under, or** & **Puncture,** & **or Trip** & **Cold** & **Injury** & **Lyby by** & **Injured by** & **Correct** \\  & & **Between** & **Scrape** & **Injury** & **Exposures** & **Injury by** & **Exposures** & **Injury by** & **Exposures** \\ \hline \multirow{4}{*}{Train} & \multirow{4}{*}{Cught In,} & \multirow{4}{*}{4} & \multirow{4}{*}{46} & \multirow{4}{*}{69} & \multirow{4}{*}{2} & \multirow{4}{*}{12} & \multirow{4}{*}{53} & \multirow{4}{*}{2.2\%}