Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia.
Computer sciences and mathematics college, University of Thi_Qar, Thi_Qar, 64000, Iraq.
Math Biosci Eng. 2023 Jan 11;20(3):5268-5297. doi: 10.3934/mbe.2023244.
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.
尽管已经建立了几个基于人工智能的 COVID-19 诊断模型，但基于机器的诊断差距仍然存在，因此必须进一步努力对抗这一流行病。因此，由于持续需要一个可靠的系统来选择特征并开发一种从临床文本预测 COVID-19 病毒的模型，我们试图创建一种新的特征选择（FS）方法。本研究采用一种受火烈鸟行为启发的新方法，寻找一个接近理想的特征子集，以准确诊断 COVID-19 患者。使用两阶段选择最佳特征。在第一阶段，我们实施了一种术语加权技术，即 RTF-C-IEF，以量化提取特征的重要性。第二阶段涉及使用一种新开发的特征选择方法，称为改进二进制火烈鸟搜索算法（IBFSA），它为 COVID-19 患者选择最重要和最相关的特征。提出的多策略改进过程是本研究的核心，旨在通过增加多样性和支持探索算法搜索空间来提高搜索算法的性能。此外，还使用二进制机制来改进传统 FSA 的性能，使其适用于二进制 FS 问题。该模型基于支持向量机（SVM）和其他分类器，使用两个包含 3053 例和 1446 例病例的数据集进行了评估。结果表明，与许多以前的群体算法相比，IBFSA 具有最佳性能。值得注意的是，所选择的特征子集数量也减少了 88%，并获得了最佳的全局最优特征。
