Predicting Sepsis in the Intensive Care Unit (ICU) through Vital Signs using Support Vector Machine (SVM)
Zeina Rayan1, *, Marco Alfonse1, Abdel-Badeeh M. Salem1
Identifiers and Pagination:Year: 2021
First Page: 108
Last Page: 113
Publisher ID: TOBIOIJ-14-108
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
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.
As sepsis is one of the life-threatening diseases, predicting sepsis with high accuracy could help save lives.
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.
The support vector machine algorithm that uses the extracted features has a great impact on sepsis prediction, which yields the accuracy of 0.73.
Predicting sepsis can be accurately performed using the main vital signs and support vector machine.