ArticleComputers in biology and medicine2021
Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A prospective cohort study develops and validates a machine learning model for predicting ecchymosis after total knee arthroplasty.Scientific reports · 2026Article
- Machine learning-based prediction of pulmonary embolism to reduce unnecessary computed tomography scans in gastrointestinal cancer patients: a retrospective multicenter study.Scientific reports · 2024Article
- A new hybrid prediction model of COVID-19 daily new case data.Engineering applications of artificial intelligence · 2023Article
- Article
- Gaussian bare-bone slime mould algorithm: performance optimization and case studies on truss structures.Artificial intelligence review · 2023Article
- Multi-Strategy Learning Boosted Colony Predation Algorithm for Photovoltaic Model Parameter Identification.Sensors (Basel, Switzerland) · 2022Article
- Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods.Expert systems · 2022Article
- Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization.Computers in biology and medicine · 2022Article
- Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine.Computers in biology and medicine · 2022Article
- Feature Selection Based on Adaptive Particle Swarm Optimization with Leadership Learning.Computational intelligence and neuroscience · 2022Article
- Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis.Computational and mathematical methods in medicine · 2022Article
- An Optimized Framework for Breast Cancer Classification Using Machine Learning.BioMed research international · 2022Article
- Fireworks explosion boosted Harris Hawks optimization for numerical optimization: Case of classifying the severity of COVID-19.Frontiers in neuroinformatics · 2022Article
- Identification of the Potential Molecular Mechanism ofComputational and mathematical methods in medicine · 2022Article
- Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022Article
- Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu.Frontiers in plant science · 2021Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Coronavirus Disease 2019 (COVID-19) was distributed globally at the end of December 2019 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early diagnosis and successful COVID-19 assessment are missing, clinical care is ineffective, and deaths are high. In this study, we investigate whether the level of biochemical indicators helps to discriminate and classify the severity of the COVID-19 using the machine learning method. This research creates an efficient intelligence method for the diagnosis of COVID-19 from the perspective of biochemical indexes. The framework is proposed by integrating an enhanced new stochastic called the colony predation algorithm (CPA) with a kernel extreme learning machine (KELM), abbreviated as ECPA-KELM. The core feature of the approach is the ECPA algorithm which incorporates the two main operators that have been abstained from the grey wolf optimizer and moth-flame optimizer to improve and restore the CPA research functions and are simultaneously used to optimize the parameters and to select features for KELM. The ECPA output is checked thoroughly using IEEE CEC2017 benchmark to verify the capacity of the proposed methodology. Finally, in the diagnosis of COVID-19 using biochemical indexes, the designed ECPA-KELM model and other competing KELM models based on other optimization are used. Checking statistical results will display improved predictive properties for all metrics and higher stability. ECPA-KELM can also be used to discriminate and classify the severity of the COVID-19 as a possible computer-aided method and provide effective early warning for the treatment and diagnosis of COVID-19.
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