Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical text

Amir Yasseen Mahdi; Siti Sophiayati Yuhaniz; Amir Yasseen Mahdi; Siti Sophiayati Yuhaniz

doi:10.3934/mbe.2023244

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 3: 5268-5297. doi: 10.3934/mbe.2023244

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Research article

Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical text

Amir Yasseen Mahdi ^{1,2
,
,},
Siti Sophiayati Yuhaniz ¹

1.
Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
2.
Computer sciences and mathematics college, University of Thi_Qar, Thi_Qar, 64000, Iraq

Academic Editor: Yang Kuang

Received: 29 October 2022 Revised: 12 December 2022 Accepted: 22 December 2022 Published: 11 January 2023

Though several AI-based models have been established for COVID-19 diagnosis, the machine-based diagnostic gap is still ongoing, making further efforts to combat this epidemic imperative. So, we tried to create a new feature selection (FS) method because of the persistent need for a reliable system to choose features and to develop a model to predict the COVID-19 virus from clinical texts. This study employs a newly developed methodology inspired by the flamingo's behavior to find a near-ideal feature subset for accurate diagnosis of COVID-19 patients. The best features are selected using a two-stage. In the first stage, we implemented a term weighting technique, which that is RTF-C-IEF, to quantify the significance of the features extracted. The second stage involves using a newly developed feature selection approach called the improved binary flamingo search algorithm (IBFSA), which chooses the most important and relevant features for COVID-19 patients. The proposed multi-strategy improvement process is at the heart of this study to improve the search algorithm. The primary objective is to broaden the algorithm's capabilities by increasing diversity and support exploring the algorithm search space. Additionally, a binary mechanism was used to improve the performance of traditional FSA to make it appropriate for binary FS issues. Two datasets, totaling 3053 and 1446 cases, were used to evaluate the suggested model based on the Support Vector Machine (SVM) and other classifiers. The results showed that IBFSA has the best performance compared to numerous previous swarm algorithms. It was noted, that the number of feature subsets that were chosen was also drastically reduced by 88% and obtained the best global optimal features.
- COVID-19,
- natural language processing,
- feature selection,
- binary flamingo search algorithm,
- clinical text classification
Citation: Amir Yasseen Mahdi, Siti Sophiayati Yuhaniz. Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical text[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 5268-5297. doi: 10.3934/mbe.2023244

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Abstract

Though several AI-based models have been established for COVID-19 diagnosis, the machine-based diagnostic gap is still ongoing, making further efforts to combat this epidemic imperative. So, we tried to create a new feature selection (FS) method because of the persistent need for a reliable system to choose features and to develop a model to predict the COVID-19 virus from clinical texts. This study employs a newly developed methodology inspired by the flamingo's behavior to find a near-ideal feature subset for accurate diagnosis of COVID-19 patients. The best features are selected using a two-stage. In the first stage, we implemented a term weighting technique, which that is RTF-C-IEF, to quantify the significance of the features extracted. The second stage involves using a newly developed feature selection approach called the improved binary flamingo search algorithm (IBFSA), which chooses the most important and relevant features for COVID-19 patients. The proposed multi-strategy improvement process is at the heart of this study to improve the search algorithm. The primary objective is to broaden the algorithm's capabilities by increasing diversity and support exploring the algorithm search space. Additionally, a binary mechanism was used to improve the performance of traditional FSA to make it appropriate for binary FS issues. Two datasets, totaling 3053 and 1446 cases, were used to evaluate the suggested model based on the Support Vector Machine (SVM) and other classifiers. The results showed that IBFSA has the best performance compared to numerous previous swarm algorithms. It was noted, that the number of feature subsets that were chosen was also drastically reduced by 88% and obtained the best global optimal features.

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