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

Zeina Rayan1, *, Marco Alfonse1, Abdel-Badeeh M. Salem1
1 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

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Creative Commons License
© 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: ( 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:



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.

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