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Predicting Sepsis in the Intensive Care Unit (ICU) through Vital Signs using Support Vector Machine (SVM)
The Open Bioinformatics Journal
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19 Nov 2021 •
RESEARCH ARTICLE
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DOI: 10.2174/18750362021140100108
Abstract
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.
