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Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection

BACKGROUND: Imbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data. Despite various studies, class imbalance has always been a difficult issue. The main objective of this study was to find an effective integrated approach to addres...

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Autores principales: Liu, Lijue, Wu, Xiaoyu, Li, Shihao, Li, Yi, Tan, Shiyang, Bai, Yongping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962101/
https://www.ncbi.nlm.nih.gov/pubmed/35346181
http://dx.doi.org/10.1186/s12911-022-01821-w
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author Liu, Lijue
Wu, Xiaoyu
Li, Shihao
Li, Yi
Tan, Shiyang
Bai, Yongping
author_facet Liu, Lijue
Wu, Xiaoyu
Li, Shihao
Li, Yi
Tan, Shiyang
Bai, Yongping
author_sort Liu, Lijue
collection PubMed
description BACKGROUND: Imbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data. Despite various studies, class imbalance has always been a difficult issue. The main objective of this study was to find an effective integrated approach to address the problems posed by class imbalance and to validate the method in an early screening model for a rare cardiovascular disease aortic dissection (AD). METHODS: Different data-level methods, cost-sensitive learning, and the bagging method were combined to solve the problem of low sensitivity caused by the imbalance of two classes of data. First, feature selection was applied to select the most relevant features using statistical analysis, including significance test and logistic regression. Then, we assigned two different misclassification cost values for two classes, constructed weak classifiers based on the support vector machine (SVM) model, and integrated the weak classifiers with undersampling and bagging methods to build the final strong classifier. Due to the rarity of AD, the data imbalance was particularly prominent. Therefore, we applied our method to the construction of an early screening model for AD disease. Clinical data of 523,213 patients from the Institute of Hypertension, Xiangya Hospital, Central South University were used to verify the validity of this method. In these data, the sample ratio of AD patients to non-AD patients was 1:65, and each sample contained 71 features. RESULTS: The proposed ensemble model achieved the highest sensitivity of 82.8%, with training time and specificity reaching 56.4 s and 71.9% respectively. Additionally, it obtained a small variance of sensitivity of 19.58 × 10(–3) in the seven-fold cross validation experiment. The results outperformed the common ensemble algorithms of AdaBoost, EasyEnsemble, and Random Forest (RF) as well as the single machine learning (ML) methods of logistic regression, decision tree, k nearest neighbors (KNN), back propagation neural network (BP) and SVM. Among the five single ML algorithms, the SVM model after cost-sensitive learning method performed best with a sensitivity of 79.5% and a specificity of 73.4%. CONCLUSIONS: In this study, we demonstrate that the integration of feature selection, undersampling, cost-sensitive learning and bagging methods can overcome the challenge of class imbalance in a medical dataset and develop a practical screening model for AD, which could lead to a decision support for screening for AD at an early stage.
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spelling pubmed-89621012022-03-30 Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection Liu, Lijue Wu, Xiaoyu Li, Shihao Li, Yi Tan, Shiyang Bai, Yongping BMC Med Inform Decis Mak Research BACKGROUND: Imbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data. Despite various studies, class imbalance has always been a difficult issue. The main objective of this study was to find an effective integrated approach to address the problems posed by class imbalance and to validate the method in an early screening model for a rare cardiovascular disease aortic dissection (AD). METHODS: Different data-level methods, cost-sensitive learning, and the bagging method were combined to solve the problem of low sensitivity caused by the imbalance of two classes of data. First, feature selection was applied to select the most relevant features using statistical analysis, including significance test and logistic regression. Then, we assigned two different misclassification cost values for two classes, constructed weak classifiers based on the support vector machine (SVM) model, and integrated the weak classifiers with undersampling and bagging methods to build the final strong classifier. Due to the rarity of AD, the data imbalance was particularly prominent. Therefore, we applied our method to the construction of an early screening model for AD disease. Clinical data of 523,213 patients from the Institute of Hypertension, Xiangya Hospital, Central South University were used to verify the validity of this method. In these data, the sample ratio of AD patients to non-AD patients was 1:65, and each sample contained 71 features. RESULTS: The proposed ensemble model achieved the highest sensitivity of 82.8%, with training time and specificity reaching 56.4 s and 71.9% respectively. Additionally, it obtained a small variance of sensitivity of 19.58 × 10(–3) in the seven-fold cross validation experiment. The results outperformed the common ensemble algorithms of AdaBoost, EasyEnsemble, and Random Forest (RF) as well as the single machine learning (ML) methods of logistic regression, decision tree, k nearest neighbors (KNN), back propagation neural network (BP) and SVM. Among the five single ML algorithms, the SVM model after cost-sensitive learning method performed best with a sensitivity of 79.5% and a specificity of 73.4%. CONCLUSIONS: In this study, we demonstrate that the integration of feature selection, undersampling, cost-sensitive learning and bagging methods can overcome the challenge of class imbalance in a medical dataset and develop a practical screening model for AD, which could lead to a decision support for screening for AD at an early stage. BioMed Central 2022-03-28 /pmc/articles/PMC8962101/ /pubmed/35346181 http://dx.doi.org/10.1186/s12911-022-01821-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Liu, Lijue
Wu, Xiaoyu
Li, Shihao
Li, Yi
Tan, Shiyang
Bai, Yongping
Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
title Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
title_full Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
title_fullStr Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
title_full_unstemmed Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
title_short Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
title_sort solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962101/
https://www.ncbi.nlm.nih.gov/pubmed/35346181
http://dx.doi.org/10.1186/s12911-022-01821-w
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