RESEARCH ARTICLE

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

The Open Bioinformatics Journal 19 Nov 2021 RESEARCH ARTICLE 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.
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