Influential Causes that Affect Largely for the Survival of a Patient with Heart-failure: A Machine Learning Perspective

Paper

Talin, Iffat Ara, Abid, Mahmudul Hasan, Awal, Md Abdul and Nahid, Abdullah-Al. 2022. "Influential Causes that Affect Largely for the Survival of a Patient with Heart-failure: A Machine Learning Perspective." 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2). Rajshahi, Bangladesh 26 - 27 Dec 2021 Bangladesh. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IC4ME253898.2021.9768462
Paper/Presentation Title

Influential Causes that Affect Largely for the Survival of a Patient with Heart-failure: A Machine Learning Perspective

Presentation TypePaper
AuthorsTalin, Iffat Ara, Abid, Mahmudul Hasan, Awal, Md Abdul and Nahid, Abdullah-Al
Journal or Proceedings TitleProceedings of 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)
Number of Pages4
Year2022
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationBangladesh
ISBN9781665406376
9781665406383
Digital Object Identifier (DOI)https://doi.org/10.1109/IC4ME253898.2021.9768462
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9768462
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9768399/proceeding
Conference/Event2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)
Event Details
2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)
Delivery
In person
Event Date
26 to end of 27 Dec 2021
Event Location
Rajshahi, Bangladesh
Abstract

Heart failure is a chronic, irreversible condition. It causes 32% of entire mortality globally. The purpose of this study is to figure out which of the characteristics of patients with heart failure impacts their survival most so that improving those traits can lead to their quality health. Machine learning can be more proficient in diagnosing it precisely and minutely. XGBoost classified the data and genetic algorithm was further introduced for feature selection. Three features have been estimated to be most significant in our study-creatinine phosphokinase, serum sodium, and sex. This unearthing can aid doctors and physicians to determine the treatment they will be providing to the sufferers. Controlling the limits of creatinine phosphokinase and serum sodium can be impactful to reduce the severity of health hazards and mortality.

KeywordsHeart failure; Genetic algorithm; XGBoost; Feature selection
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
Public Notes

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Byline AffiliationsKhulna University, Bangladesh
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