Universiti Teknologi Malaysia Institutional Repository

Deep learning classification of biomedical text using convolutional neural network

Dollah, Rozilawati and Sheng, Chew Yi and Zakaria, Norhawaniah and Othman, Mohd. Shahizan and Rasib, Abd. Wahid (2019) Deep learning classification of biomedical text using convolutional neural network. International Journal of Advanced Computer Science and Applications, 10 (8). pp. 512-517. ISSN 2158-107X


Official URL: http://dx.doi.org/10.14569/ijacsa.2019.0100867


In this digital era, the document entries have been increasing days by days, causing a situation where the volume of the document entries in overwhelming. This situation has caused people to encounter with problems such as congestion of data, difficulty in searching the intended information or even difficulty in managing the databases, for example, MEDLINE database which stores the documents related to the biomedical field. This research will specify the solution focusing in text classification of the biomedical abstracts. Text classification is the process of organizing documents into predefined classes. A standard text classification framework consists of feature extraction, feature selection and the classification stages. The dataset used in this research is the Ohsumed dataset which is the subset of the MEDLINE database. In this research, there is a total number of 11,566 abstracts selected from the Ohsumed dataset. First of all, feature extraction is performed on the biomedical abstracts and a list of unique features is produced. All the features in this list will be added to the multiword tokenizer lexicon for tokenizing phrases or compound word. After that, the classification of the biomedical texts is conducted using the deep learning network, Convolutional Neural Network which is an approach widely used in many domains such as pattern recognition, classification and so on. The goal of classification is to accurately organize the data into the correct predefined classes. The Convolutional Neural Network has achieved a result of 54.79% average accuracy, 61.00% average precision, 60.00% average recall and 60.50% average F1-score. In short, it is hoped that this research could be beneficial to the text classification area.

Item Type:Article
Uncontrolled Keywords:Compound term, Ohsumed dataset
Subjects:T Technology > T Technology (General)
ID Code:87815
Deposited By: Widya Wahid
Deposited On:30 Nov 2020 21:21
Last Modified:30 Nov 2020 21:21

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