Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network
TABLE III: Comparative Results of Our RCN Model and Baseline Methods
['Method', 'Input', 'P', 'R', 'F{}_{1}']
First of all, we can observe that our model with entity type features outperforms these reference algorithms, with 95.59% in Precision, 97.45% in Recall and 96.51% in F{}_{1}-score. The improvements compared with the original baselines without entity type features are 1.5, 1.5, 1.05, 0.45 and 1.2 points in Recall, 0.54, 1.64, 2.53, 1.63 and 1.77 points in F{}_{1}-score, respectively. Without entity type features, our model also outperforms the reference algorithms without entity type features. Both the Recall and F{}_{1}-score of our model achieve the best ones, and the Precision of our model is just below the CNN + MaxPooling method, which use an extra position feature, while our model does not. Secondly, the entity type features proposed by us can help improve performance of the original baselines. The benefits in F{}_{1}-score brought by the entity type features are 0.27, 0.87, 2.28, 0.69 and 1.22 points, respectively. However, the improved performance is still worse than our model. Thirdly, among the baselines, it is interesting to note that without entity type features, attention-based pooling technique performs worse than conventional max-pooling strategy, which has also been observed earlier by Sahu and Anand [sahu2017drug] and Raj et al. [raj2017learning], while with entity type features, attention-based pooling technique performs better than conventional max-pooling strategy.