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Multi-class Classification Of Covid-19 Documents Using Machine Learning Algorithms.

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Multi-class Classification Of Covid-19 Documents Using Machine Learning Algorithms.

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Multi-class classification of COVID-19 documents using machine learning algorithms.

Gollam Rabby¹, Petr Berka¹

¹Department of Information and Knowledge Engineering, Prague University of Economics and Business, Prague, Czech Republic.

Journal of Intelligent Information Systems

|December 5, 2022

View abstract on PubMed

Summary

This summary is machine-generated.

TF-IDF representations and abstracts are effective for biomedical document classification. Random Forest and BERT neural networks offer the best performance, guiding practitioners in this crucial task.

Keywords:

COVID-19 Machine learning algorithms Multi-class classification Text mining

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Area of Science:

Biomedical Informatics
Natural Language Processing
Machine Learning

Background:

Accurate and rapid identification of relevant scientific research papers is critical in biomedical research, especially during global health crises.
Traditional text classifiers are insufficient for biomedical documents, necessitating the integration of diverse data sources like entities and bibliometric data.
Developing effective biomedical document classification systems aids researchers and learners in categorizing and retrieving scientific literature efficiently.

Purpose of the Study:

To investigate the influence of different information types and feature representation methods on biomedical document classification.
To compare the effectiveness of various features, including text, entities, and bibliometric data, for classifying biomedical research papers.
To provide practical guidelines for optimizing biomedical document classification systems.

Main Methods:

Experiments were conducted using conventional text classification methods with features extracted from titles, abstracts, and bibliometric data.
Data preprocessing involved cleaning, feature engineering, and multi-class classification across eleven input data variants.
Ten machine learning algorithms were employed to analyze the performance, data efficiency, and interpretability of different models.

Main Results:

TF-IDF (Term Frequency-Inverse Document Frequency) representations demonstrated superior performance compared to entity extraction methods.
The abstract content alone proved sufficient for accurate document classification.
Random Forest and Neural Network (BERT) algorithms achieved the highest performance across various document representations.

Conclusions:

Biomedical document classification can be effectively achieved using TF-IDF features derived from abstracts, outperforming entity-based approaches.
Random Forest and BERT models provide robust and accurate classification, offering practical solutions for managing biomedical literature.
The study offers concrete guidelines for practitioners to enhance biomedical document classification, particularly relevant for managing research during health crises like COVID-19.