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Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., et al. (2020) Development and Evaluation of an Artificial Intelligence System for Covid-19 Diagnosis. Nature Communications, 11, Article No. 5088.
https://doi.org/10.1038/s41467-020-18685-1

has been cited by the following article:

• TITLE: Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

  AUTHORS: Hend S. Salem, Mohamed A. Mead, Ghada S. El-Taweel

  KEYWORDS: Big COVID-19 Data, Machine Learning, Hyperparameter Optimization, Particle Swarm Optimization, Computational Intelligence

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.3, March 27, 2024

  ABSTRACT: Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.