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Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis
Neonatal necrotizing enterocolitis (NEC) occurs worldwide and is a major source of neonatal morbidity and mortality. Researchers have developed many methods for predicting NEC diagnosis and prognosis. However, most people use statistical methods to select features, which may ignore the correlation b...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9390903/ https://www.ncbi.nlm.nih.gov/pubmed/35984833 http://dx.doi.org/10.1371/journal.pone.0273383 |
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author | Song, Jianfei Li, Zhenyu Yao, Guijin Wei, Songping Li, Ling Wu, Hui |
author_facet | Song, Jianfei Li, Zhenyu Yao, Guijin Wei, Songping Li, Ling Wu, Hui |
author_sort | Song, Jianfei |
collection | PubMed |
description | Neonatal necrotizing enterocolitis (NEC) occurs worldwide and is a major source of neonatal morbidity and mortality. Researchers have developed many methods for predicting NEC diagnosis and prognosis. However, most people use statistical methods to select features, which may ignore the correlation between features. In addition, because they consider a small dimension of characteristics, they neglect some laboratory parameters such as white blood cell count, lymphocyte percentage, and mean platelet volume, which could be potentially influential factors affecting the diagnosis and prognosis of NEC. To address these issues, we include more perinatal, clinical, and laboratory information, including anemia—red blood cell transfusion and feeding strategies, and propose a ridge regression and Q-learning strategy based bee swarm optimization (RQBSO) metaheuristic algorithm for predicting NEC diagnosis and prognosis. Finally, a linear support vector machine (linear SVM), which specializes in classifying high-dimensional features, is used as a classifier. In the NEC diagnostic prediction experiment, the area under the receiver operating characteristic curve (AUROC) of dataset 1 (feeding intolerance + NEC) reaches 94.23%. In the NEC prognostic prediction experiment, the AUROC of dataset 2 (medical NEC + surgical NEC) reaches 91.88%. Additionally, the classification accuracy of the RQBSO algorithm on the NEC dataset is higher than the other feature selection algorithms. Thus, the proposed approach has the potential to identify predictors that contribute to the diagnosis of NEC and stratification of disease severity in a clinical setting. |
format | Online Article Text |
id | pubmed-9390903 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-93909032022-08-20 Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis Song, Jianfei Li, Zhenyu Yao, Guijin Wei, Songping Li, Ling Wu, Hui PLoS One Research Article Neonatal necrotizing enterocolitis (NEC) occurs worldwide and is a major source of neonatal morbidity and mortality. Researchers have developed many methods for predicting NEC diagnosis and prognosis. However, most people use statistical methods to select features, which may ignore the correlation between features. In addition, because they consider a small dimension of characteristics, they neglect some laboratory parameters such as white blood cell count, lymphocyte percentage, and mean platelet volume, which could be potentially influential factors affecting the diagnosis and prognosis of NEC. To address these issues, we include more perinatal, clinical, and laboratory information, including anemia—red blood cell transfusion and feeding strategies, and propose a ridge regression and Q-learning strategy based bee swarm optimization (RQBSO) metaheuristic algorithm for predicting NEC diagnosis and prognosis. Finally, a linear support vector machine (linear SVM), which specializes in classifying high-dimensional features, is used as a classifier. In the NEC diagnostic prediction experiment, the area under the receiver operating characteristic curve (AUROC) of dataset 1 (feeding intolerance + NEC) reaches 94.23%. In the NEC prognostic prediction experiment, the AUROC of dataset 2 (medical NEC + surgical NEC) reaches 91.88%. Additionally, the classification accuracy of the RQBSO algorithm on the NEC dataset is higher than the other feature selection algorithms. Thus, the proposed approach has the potential to identify predictors that contribute to the diagnosis of NEC and stratification of disease severity in a clinical setting. Public Library of Science 2022-08-19 /pmc/articles/PMC9390903/ /pubmed/35984833 http://dx.doi.org/10.1371/journal.pone.0273383 Text en © 2022 Song et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Song, Jianfei Li, Zhenyu Yao, Guijin Wei, Songping Li, Ling Wu, Hui Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis |
title | Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis |
title_full | Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis |
title_fullStr | Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis |
title_full_unstemmed | Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis |
title_short | Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis |
title_sort | framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9390903/ https://www.ncbi.nlm.nih.gov/pubmed/35984833 http://dx.doi.org/10.1371/journal.pone.0273383 |
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