Deep Learning Classification of Biomedical Text using Convolutional Neural Network
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 8
Abstract
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.
Authors and Affiliations
Rozilawati Dollah, Chew Yi Sheng, Norhawaniah Zakaria, Mohd Shahizan Othman, Abd Wahid Rasib
Keywords
Related Articles
- EP ID EP626826
- DOI 10.14569/IJACSA.2019.0100867
- Views 310
- Downloads 0
Dimensionality Reduction using Hybrid Support Vector Machine and Discriminant Independent Component Analysis for Hyperspectral Image
Hyperspectral image is an image obtain from a satellite sensor. This image has more than 100 bands with a wide spectral range and increased spatial image resolution, providing detailed information on objects or materials...
Effective Teaching Methods and Proposed Web Libraries for Designing Animated Course Content: A Review
The primary aim of education system is to improve cognitive and computational skills in students. It cannot be achieved by just using the latest technology. This goal can only be achieved through effective teaching metho...
An Information Hiding Scheme Based on Pixel-Value-Ordering and Prediction-Error Expansion with Reversibility
This paper proposes a data hiding scheme based on pixel-value-ordering and predication-error expansion. In a natural image, most neighboring pixels have similar pixel values, i.e. the difference between neighboring pixel...
A Proposed Integrated Approach for BI and GIS in Health Sector to Support Decision Makers (BIGIS-DSS)
This paper explores the possibilities of adopting Business Intelligence (BI), and Geographic Information System (GIS) to build a spatial intelligence and predictive analytical approach. The proposed approach will help in...
Suitable Personality Traits for Learning Programming Subjects: A Rough-Fuzzy Model
Programming is a cognitive activity which requires logical reasoning to code for abstract presentation. This study aims to find out the personality traits of students who maintain the effective grades in learning program...