  We report applications of Convolutional Neural Networks (CNN) to
multi-classification classification of a large medical data set. We discuss in
detail how changes in the CNN model and the data pre-processing impact the
classification results. In the end, we introduce an ensemble model that
consists of both deep learning (CNN) and shallow learning models (Gradient
Boosting). The method achieves Accuracy of 64.93, the highest three-class
classification accuracy we achieved in this study. Our results also show that
CNN and the ensemble consistently obtain a higher Recall than Precision. The
highest Recall is 68.87, whereas the highest Precision is 65.04.
