A novel combined dynamic ensemble selection model for imbalanced data to detect COVID-19 from complete blood count.
Comput Methods Programs Biomed
; 211: 106444, 2021 Nov.
Article
in English
| MEDLINE | ID: covidwho-1439955
ABSTRACT
BACKGROUND:
As blood testing is radiation-free, low-cost and simple to operate, some researchers use machine learning to detect COVID-19 from blood test data. However, few studies take into consideration the imbalanced data distribution, which can impair the performance of a classifier.METHOD:
A novel combined dynamic ensemble selection (DES) method is proposed for imbalanced data to detect COVID-19 from complete blood count. This method combines data preprocessing and improved DES. Firstly, we use the hybrid synthetic minority over-sampling technique and edited nearest neighbor (SMOTE-ENN) to balance data and remove noise. Secondly, in order to improve the performance of DES, a novel hybrid multiple clustering and bagging classifier generation (HMCBCG) method is proposed to reinforce the diversity and local regional competence of candidate classifiers.RESULTS:
The experimental results based on three popular DES methods show that the performance of HMCBCG is better than only use bagging. HMCBCG+KNE obtains the best performance for COVID-19 screening with 99.81% accuracy, 99.86% F1, 99.78% G-mean and 99.81% AUC.CONCLUSION:
Compared to other advanced methods, our combined DES model can improve accuracy, G-mean, F1 and AUC of COVID-19 screening.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Diagnostic study
Limits:
Humans
Language:
English
Journal:
Comput Methods Programs Biomed
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
J.cmpb.2021.106444
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