Performance Evaluation of Deep Learning Algorithms in Biomedical Document Classification

• Published in: 2019 11th International Conference on Advanced Computing (ICoAC) pp. 220 - 224
• Main Authors: Behera, Bichitrananda, Kumaravelan, G., Kumar B., Prem
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2019
• Subjects: Accuracy; Artificial neural networks; Benchmark testing; Biological system modeling; Classification algorithms; Deep learning; ensemble classifier; machine learning; Natural language processing; Performance evaluation; Recurrent neural networks; text classification; Vectors
• DOI: 10.1109/ICoAC48765.2019.246843

Document classification is a prevalent task in Natural Language Processing (NLP), which has an extensive range of applications in the biomedical domains such as biomedical literature indexing, automatic diagnosis codes assignment, tweets classification for public health topics, and patient safety reports classification. Nevertheless, manual classification of biomedical articles published every year into specific predefined categories becomes a cumbersome task. Hence, building an automatic document classification for biomedical databases emerges as a significant task among the scientific community. In recent years, Deep Learning (DL) models like Deep Neural Networks (DNN), Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), and Ensemble Deep Learning models are widely used in the area of text document classification for better classification performance compared to Machine Learning (ML) algorithms. The major advantage of using DL models in document classification is that it provides rich semantic and grammatical information for document representation through pre-trained word embedding. Hence, this paper investigates the deployment of the various state-of-the-art DL based classification models in automatic classification of benchmark biomedical datasets. Finally, the performance of all the aforementioned constitutional classifiers is compared and evaluated through the well-defined performance evaluation metrics such as accuracy, precision, recall, and f1measure.