Open AccessPredicting Sepsis in the Intensive Care Unit (ICU) through Vital Signs using Support Vector Machine (SVM)
Author(s) -
Zeina Rayan,
Marco Alfonse,
Abdel-Badeeh M. Salem
Publication year - 2021
Publication title -
the open bioinformatics journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 8
ISSN - 1875-0362
DOI - 10.2174/18750362021140100108
Subject(s) - support vector machine , sepsis , intensive care unit , computer science , artificial intelligence , vital signs , relevance vector machine , machine learning , feature (linguistics) , feature selection , intensive care medicine , pattern recognition (psychology) , medicine , immunology , surgery , linguistics , philosophy
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.