ArticlePattern recognition2021
Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy.
Article in Pattern recognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm-Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor.Contrast media & molecular imaging · 2022Trial
- An optimized method for mulberry silkworm, Bombyx mori (Bombycidae:Lepidoptera) sex classification using TLBPSGA-RFEXGBoost.Biology open · 2024Article
- Indonesian presidential election sentiment: Dataset of response public before 2024.Data in brief · 2024Article
- COVID-19 and Rumors: A Dynamic Nested Optimal Control Model.Pattern recognition · 2023Article
- COVID-19 contact tracking by group activity trajectory recovery over camera networks.Pattern recognition · 2022Article
- Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision.Expert systems with applications · 2022Article
- Expecting individuals' body reaction to Covid-19 based on statistical Naïve Bayes technique.Pattern recognition · 2022Article
- ISW-LM: An intensive symptom weight learning mechanism for early COVID-19 diagnosis.Computers in biology and medicine · 2022Article
- The Influence of Environmental Factors on the Spread of COVID-19 in Italy.Procedia computer science · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Early detection of COVID-19 patients is an important issue for treating and controlling the disease from spreading. In this paper, a new strategy for detecting COVID-19 infected patients will be introduced, which is called Distance Biased Naïve Bayes (DBNB). The novelty of DBNB as a proposed classification strategy is concentrated in two contributions. The first is a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients. APSO is a hybrid method based on both filter and wrapper methods to provide accurate and significant features for the next classification phase. The considered features are extracted from Laboratory findings for different cases of people, some of whom are COVID-19 infected while some are not. APSO consists of two sequential feature selection stages, namely; Initial Selection Stage (IS
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