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Medical speciality classification system based on binary particle swarms and ensemble of one vs. rest support vector machines.

Abstract：

:Nowadays, artificial intelligence plays an integral role in medical and healthcare informatics. Developing an automatic question classification and answering system is essential for coping with constant advancements in science and technology. However, efficient online medical services are required to promote offline medical services. This article proposes a system that automatically classifies medical questions of patients into medical specialities and supports the Arabic language in the MENA region. Text classification is not trivial, especially when dealing with a highly morphologically complex language, the dialectical form of which is the dominant form on the Internet. This work utilizes 15,000 medical questions asked by the clients of Altibbi telemedicine company. The questions are classified into 15 medical specialities. As the number of medical questions received daily by the company has increased, a need has arisen for an automatic classification system that can save the medical personnel much time and effort. Therefore, this article presents an efficient medical speciality classification system based on swarm intelligence (SI) and an ensemble of support vector machines (SVMs). Particle swarm optimization (PSO) is an SI-based and stochastic metaheuristic algorithm that is adopted to search for the optimal number of features and tune the hyperparameters of the SVM classifiers, which are deployed as one-versus-rest for multi-class classification. In addition, PSO is integrated with various binarization techniques to boost its performance. The experimental results show that the proposed approach accomplished remarkable performance as it achieved an accuracy of 85% and a features reduction rate of 95.9%.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Faris H,Habib M,Faris M,Alomari M,Alomari A

doi

10.1016/j.jbi.2020.103525

subject

Has Abstract

pub_date

2020-09-01 00:00:00

pages

103525

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(20)30153-2

journal_volume

109

pub_type

杂志文章

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