Automatic Classification of Pathology Reports using TF-IDF Features
TABLE III: Final train and test performance of classification models
['Classifier Code', 'Micro F-score Train', 'Micro F-score Test', 'Macro F-score Train', 'Macro F-score Test']
A classification model is trained to predict the primary diagnosis given the content of the cancer pathology report. We can observe that the XGBoost classifier outperformed all other models for both the micro F-score metric, with a score of 0.92, and the macro F-score metric, with a score of 0.31. This was an improvement of 7% for the micro F-score over the next best model, SVM-L, and a marginal improvement of 5% for macro F-score. It is interesting to note that SVM with linear kernels performs much better than SVM with RBF kernel, scoring 9% on the macro F-score and 12% more on the micro F-score. It is suspected that since words used in primary diagnosis itself occur in some reports, thus enabling the linear models to outperform complex models.