ArticleIEEE access : practical innovations, open solutions2021
Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Article
- Article
- A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms.Mobile networks and applications : MONET · 2023Article
- ABOA-CNN: auction-based optimization algorithm with convolutional neural network for pulmonary disease prediction.Neural computing & applications · 2023Article
- Biserial targeted feature projection based radial kernel regressive deep belief neural learning for covid-19 prediction.Soft computing · 2023Article
- Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data.Neural computing & applications · 2023Article
- Henry gas solubility optimization double machine learning classifier for neurosurgical patients.PloS one · 2023Article
- Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine.Computers in biology and medicine · 2022Article
- bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.Frontiers in neuroinformatics · 2022Article
- Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis.Scientific reports · 2021Article
- Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation.Computers in biology and medicine · 2021Article
- An Effective Machine Learning Approach for Identifying Non-Severe and Severe Coronavirus Disease 2019 Patients in a Rural Chinese Population: The Wenzhou Retrospective Study.IEEE access : practical innovations, open solutions · 2021Article
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Authors and funding
18 authors.
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Abstract
This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.
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