Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network
TABLE VI: Comparisons between Different Input Features with Different Routing Iterations
['r', 'Word Only P', 'Word Only R', 'Word Only F{}_{1}', 'Word + Entity Type P', 'Word + Entity Type R', 'Word + Entity Type F{}_{1}']
To study the effect of the input features and the routing iterations in our model, we experimentally compare the performance between different input features with different routing iterations. The Precision, Recall and F{}_{1}-score are 95.59%, 97.45% and 96.51%, respectively. Comparing the model inputs, we can observe that the performance of our model with entity type features is better than that without entity type features. The benefits are 0.66 in Precision, 0.63 in Recall and 0.65 in F{}_{1}-score on average. Comparing the five lines in the table, we can observe that whatever inputs are used, the performance (in terms of F{}_{1}-score) first grows then drops down with the increase of iterative number r. When words are only used, our model achieves its best performance with r=2. And when words and entity types are both used, our model achieves its best performance with r=4.