GWO-XGB: Grey Wolf Optimization-based eXtreme Gradient Boosting for Hypertension Prediction in Bangladesh

Paper

Tahsin, Tasfia, Mumenin, Khondoker Mirazul, Pinki, Farhana Tazmim, Tuli, Anamika Biswas, Sikder, Shahriar, Rahman, Md Ashfikur, Bulbul, Abdullah Al-Mamun and Awal, Md Abdul. 2021. "GWO-XGB: Grey Wolf Optimization-based eXtreme Gradient Boosting for Hypertension Prediction in Bangladesh." 2021 International Conference on Electronics, Communications and Information Technology (ICECIT). Khulna, Bangladesh 14 - 16 Sep 2021 Bangladesh. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICECIT54077.2021.9641256
Paper/Presentation Title

GWO-XGB: Grey Wolf Optimization-based eXtreme Gradient Boosting for Hypertension Prediction in Bangladesh

Presentation TypePaper
AuthorsTahsin, Tasfia, Mumenin, Khondoker Mirazul, Pinki, Farhana Tazmim, Tuli, Anamika Biswas, Sikder, Shahriar, Rahman, Md Ashfikur, Bulbul, Abdullah Al-Mamun and Awal, Md Abdul
Journal or Proceedings TitleProceedings of 2021 International Conference on Electronics, Communications and Information Technology (ICECIT)
Number of Pages4
Year2021
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationBangladesh
ISBN9781665423632
9781665423649
Digital Object Identifier (DOI)https://doi.org/10.1109/ICECIT54077.2021.9641256
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9641256
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9641074/proceeding
Conference/Event2021 International Conference on Electronics, Communications and Information Technology (ICECIT)
Event Details
2021 International Conference on Electronics, Communications and Information Technology (ICECIT)
Delivery
In person
Event Date
14 to end of 16 Sep 2021
Event Location
Khulna, Bangladesh
Event Venue
Khulna University
Event Web Address (URL)
Abstract

Hypertension is rapidly increasing day by day worldwide as well as in Bangladesh. The majority of people in our country die due to hypertension. So, early prediction of this disease is a very important task that may reduce the number of affected patients. In our paper, we have proposed the Grey Wolf Optimization-based eXtreme Gradient Boosting (GWO-XGB) model which can predict hypertension based on Bangladeshi data collected in 2017–18 (BDHS'17-18). Here the hyperparameters of XGB are optimized using GWO and we have attained 91.26% accuracy in hypertension classification using this model. We have calculated performance evaluation metrics (Accuracy, error score, F1 score, Kappa score, MCC score, sensitivity, specificity) and plotted the precision-recall curve, bootstrap ROC curve to compare the performance of the proposed GWO-XGB model with some of the state-of-the-art classifiers. This study has also calculated the most influencing features to predict hypertension which will assist national policy-maker to provide more emphasis to those significant features.

KeywordsHypertension prediction; Grey Wolf Optimization; GWO-XGB algorithm; Accuracy; Feature importance
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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