ArticleBiomimetics (Basel, Switzerland)2023
Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model.
Article in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
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- Optimizing high dimensional data classification with a hybrid AI driven feature selection framework and machine learning schema.Scientific reports · 2025Article
- COVID-19 mortality and nutrition through predictive modeling and optimization based on grid search.Scientific reports · 2025Article
- Fostering trust and interpretability: integrating explainable AI (XAI) with machine learning for enhanced disease prediction and decision transparency.Diagnostic pathology · 2025Article
- Predicting stunting status among under five children in ethiopia using ensemblemachine learning algorithms.Scientific reports · 2025Article
- Thyroid disease classification using generative adversarial networks and Kolmogorov-Arnold network for three-class classification.BMC medical informatics and decision making · 2025Article
- A holistic framework for intradialytic hypotension prediction using generative adversarial networks-based data balancing.BMC medical informatics and decision making · 2025Article
- Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters.Scientific reports · 2025Article
- Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach.Scientific reports · 2025Article
- Key factors in predictive analysis of cardiovascular risks in public health.Scientific reports · 2025Article
- Increased risk of nonalcoholic fatty liver disease in patients with thyroid cancer: a nationwide cohort study.BMC cancer · 2025Article
- OptiStack classifier: optimized stacking framework with ensemble feature engineering for enhanced cardiovascular risk prediction.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Article
- Improvement of metaphor understanding via a cognitive linguistic model based on hierarchical classification and artificial intelligence SVM.Scientific reports · 2025Article
- Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS.Scientific reports · 2025Article
- A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems.Scientific reports · 2025Article
- Optimized deep residual networks for early detection of myocardial infarction from ECG signals.BMC cardiovascular disorders · 2025Article
- Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
During the pandemic of the coronavirus disease (COVID-19), statistics showed that the number of affected cases differed from one country to another and also from one city to another. Therefore, in this paper, we provide an enhanced model for predicting COVID-19 samples in different regions of Saudi Arabia (high-altitude and sea-level areas). The model is developed using several stages and was successfully trained and tested using two datasets that were collected from Taif city (high-altitude area) and Jeddah city (sea-level area) in Saudi Arabia. Binary particle swarm optimization (BPSO) is used in this study for making feature selections using three different machine learning models, i.e., the random forest model, gradient boosting model, and naive Bayes model. A number of predicting evaluation metrics including accuracy, training score, testing score, F-measure, recall, precision, and receiver operating characteristic (ROC) curve were calculated to verify the performance of the three machine learning models on these datasets. The experimental results demonstrated that the gradient boosting model gives better results than the random forest and naive Bayes models with an accuracy of 94.6% using the Taif city dataset. For the dataset of Jeddah city, the results demonstrated that the random forest model outperforms the gradient boosting and naive Bayes models with an accuracy of 95.5%. The dataset of Jeddah city achieved better results than the dataset of Taif city in Saudi Arabia using the enhanced model for the term of accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.