Predicting Sepsis in the Intensive Care Unit (ICU) through Vital Signs using Support Vector Machine (SVM)

Rayan, Zeina; Alfonse, Marco; Salem, Abdel-Badeeh M.

Predicting Sepsis in the Intensive Care Unit (ICU) through Vital Signs using Support Vector Machine (SVM)

Zeina Rayan^{1, *}, Marco Alfonse¹, Abdel-Badeeh M. Salem¹

¹ Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

Article Information

Identifiers and Pagination:

Year: 2021
Volume: 14
Issue: Suppl-M1
First Page: 108
Last Page: 113
Publisher ID: TOBIOIJ-14-108
DOI: 10.2174/18750362021140100108

Article History:

Received Date: 14/12/2020
Revision Received Date: 7/4/2021
Acceptance Date: 7/5/2021
Electronic publication date: 19/11/2021
Collection year: 2021

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© 2021 Rayan et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

^* Address correspondence to this author at the Department Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt; E-mail: zeinarayan@cis.asu.edu.eg

Background:

As sepsis is one of the life-threatening diseases, predicting sepsis with high accuracy could help save lives.

Methods:

Efficiency and accuracy of predicting sepsis can be enhanced through optimal feature selection. In this work, a support vector machine model is proposed to automatically predict a patient’s risk of sepsis based on physiological data collected from the ICU.

Results:

The support vector machine algorithm that uses the extracted features has a great impact on sepsis prediction, which yields the accuracy of 0.73.

Conclusion:

Predicting sepsis can be accurately performed using the main vital signs and support vector machine.

Keywords: Sepsis prediction, Machine learning, Artificial intelligence, Intensive care unit, Medical informatics, Smart health.

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RESEARCH ARTICLE

Predicting Sepsis in the Intensive Care Unit (ICU) through Vital Signs using Support Vector Machine (SVM)

Article Information

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Abstract

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Conclusion:

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