Handling of derived imbalanced dataset using XGBoost for identification of pulmonary embolism-a non-cardiac cause of cardiac arrest.
Ensemble
Heart failure
Pulmonary embolism
Stroke volume
Journal
Medical & biological engineering & computing
ISSN: 1741-0444
Titre abrégé: Med Biol Eng Comput
Pays: United States
ID NLM: 7704869
Informations de publication
Date de publication:
Feb 2022
Feb 2022
Historique:
received:
17
05
2021
accepted:
07
10
2021
pubmed:
14
1
2022
medline:
21
1
2022
entrez:
13
1
2022
Statut:
ppublish
Résumé
Relationship between pulmonary embolism and heart failure is presented in this paper. The proposed research is divided into two phases. The first phase includes the establishment of a novel database with the help of a Cleveland's database for cardiology in order to establish a link between pulmonary embolism and heart failure. The connectivity is based on the relationship between the stroke volume and the pulse pressure (Pp < 25% (ap_hi)). The second phase includes the applicability of machine learning on the novel database. Novel database formed in this work is imbalanced, resulting in the overfitting problem. XGBoost has been used to get rid of overfitting problem. Efficiency has been increased by formulating an ensemble technique by combining extreme learning machines, IB3 tree, logistic regression, and averaged neural network (avNNet) models.
Identifiants
pubmed: 35023074
doi: 10.1007/s11517-021-02455-2
pii: 10.1007/s11517-021-02455-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
551-558Informations de copyright
© 2021. International Federation for Medical and Biological Engineering.
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