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Firdous N, Bhardwaj S. Handling of derived imbalanced dataset using XGBoost for identification of pulmonary embolism-a non-cardiac cause of cardiac arrest. Med Biol Eng Comput. 2022;doi: 10.1007/s11517-021-02455-2.
Firdous, N., & Bhardwaj, S. (2022). Handling of derived imbalanced dataset using XGBoost for identification of pulmonary embolism-a non-cardiac cause of cardiac arrest. Medical & biological engineering & computing, . https://doi.org/10.1007/s11517-021-02455-2
Firdous, Naira, and Bhardwaj, Sushil. "Handling of derived imbalanced dataset using XGBoost for identification of pulmonary embolism-a non-cardiac cause of cardiac arrest." Medical & biological engineering & computing vol. (2022). doi: https://doi.org/10.1007/s11517-021-02455-2
Firdous N, Bhardwaj S. Handling of derived imbalanced dataset using XGBoost for identification of pulmonary embolism-a non-cardiac cause of cardiac arrest. Med Biol Eng Comput. 2022 Jan 13; doi: 10.1007/s11517-021-02455-2. Epub 2022 Jan 13. PMID: 35023074.
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Med Biol Eng Comput. 2022 Jan 13; doi: 10.1007/s11517-021-02455-2. Epub 2022 Jan 13.

Handling of derived imbalanced dataset using XGBoost for identification of pulmonary embolism-a non-cardiac cause of cardiac arrest.

Medical & biological engineering & computing

Naira Firdous, Sushil Bhardwaj

Affiliations

  1. RIMT University, Mandi Gobindgarh, India. [email protected].
  2. RIMT University, Mandi Gobindgarh, India.

PMID: 35023074 DOI: 10.1007/s11517-021-02455-2

Abstract

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.

© 2021. International Federation for Medical and Biological Engineering.

Keywords: Ensemble; Heart failure; Pulmonary embolism; Stroke volume

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