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

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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.

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Abbreviations

ap_hi :

Systolic blood pressure

ap_lo :

Diastolic blood pressure

Pp :

Pulse pressure

sv :

Stroke volume

pe :

Pulmonary embolism

HF:

Heart Failure

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Authors and Affiliations

  1. RIMT University, Mandi Gobindgarh, India

    Naira Firdous & Sushil Bhardwaj

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  1. Naira Firdous
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  2. Sushil Bhardwaj
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Correspondence to Naira Firdous.

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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 60, 551–558 (2022). https://doi.org/10.1007/s11517-021-02455-2

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  • DOI: https://doi.org/10.1007/s11517-021-02455-2

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