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An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation

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conference contribution
posted on 26.05.2021, 01:31 by Mahdi Abdollahi, Xiaoying Gao, Yi Mei, S Ghosh, J Li
Document classification (DC) is the task of assigning pre-defined labels to unseen documents by utilizing a model trained on the available labeled documents. DC has attracted much attention in medical fields recently because many issues can be formulated as a classification problem. It can assist doctors in decision making and correct decisions can reduce the medical expenses. Medical documents have special attributes that distinguish them from other texts and make them difficult to analyze. For example, many acronyms and abbreviations, and short expressions make it more challenging to extract information. The classification accuracy of the current medical DC methods is not satisfactory. The goal of this work is to enhance the input feature sets of the DC method to improve the accuracy. To approach this goal, a novel two-stage approach is proposed. In the first stage, a domain-specific dictionary, namely the Unified Medical Language System (UMLS), is employed to extract the key features belonging to the most relevant concepts such as diseases or symptoms. In the second stage, PSO is applied to select more related features from the extracted features in the first stage. The performance of the proposed approach is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set which is a widely used medical text dataset. The experimental results show substantial improvement by the proposed method on the accuracy of classification.

History

Preferred citation

Abdollahi, M., Gao, X., Mei, Y., Ghosh, S. & Li, J. (2019, June). An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings 2019 IEEE Congress on Evolutionary Computation (CEC) (00 pp. 119-126). IEEE. https://doi.org/10.1109/CEC.2019.8790259

Conference name

2019 IEEE Congress on Evolutionary Computation (CEC)

Conference start date

10/06/2019

Conference finish date

13/06/2019

Title of proceedings

2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Volume

00

Publication or Presentation Year

01/06/2019

Pagination

119-126

Publisher

IEEE

Publication status

Published