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Support Vector Machines versus Multi-layer Perceptrons for Reducing False Alarms in Intensive Care Units

by Ben Rejab Fahmi, Nouira Kaouther, Abdelwahed Trabelsi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 11
Year of Publication: 2012
Authors: Ben Rejab Fahmi, Nouira Kaouther, Abdelwahed Trabelsi
10.5120/7675-0969

Ben Rejab Fahmi, Nouira Kaouther, Abdelwahed Trabelsi . Support Vector Machines versus Multi-layer Perceptrons for Reducing False Alarms in Intensive Care Units. International Journal of Computer Applications. 49, 11 ( July 2012), 41-47. DOI=10.5120/7675-0969

@article{ 10.5120/7675-0969,
author = { Ben Rejab Fahmi, Nouira Kaouther, Abdelwahed Trabelsi },
title = { Support Vector Machines versus Multi-layer Perceptrons for Reducing False Alarms in Intensive Care Units },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 11 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 41-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number11/7675-0969/ },
doi = { 10.5120/7675-0969 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:46:03.567730+05:30
%A Ben Rejab Fahmi
%A Nouira Kaouther
%A Abdelwahed Trabelsi
%T Support Vector Machines versus Multi-layer Perceptrons for Reducing False Alarms in Intensive Care Units
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 11
%P 41-47
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a comparative study between two well-known classification techniques in the machine learning area namely the Multi-Layers Perceptrons (MLP) and the Support Vector Machines (SVM) applied in the medicine field. More precisely, our aim in this paper is to reduce the rate of false alarms in the Intensive Care Units (ICU) using the SVM and the MLP techniques. To this end, we have performed an appropriate setting of parameters for both SVM and MLP techniques to guarantee the good monitoring of patients' states. Then, we have made a comparison between the adapted classification techniques i. e. the SVM and the MLP and the current system using different evaluation criteria. Results of comparative experiments show that the true alarms can be identified with high accuracy by the SVM technique. Compared with the MLP and the current system, the SVM technique shows its potential to reduce the rate of false alarms.

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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Support Vector Machines Multi-Layers Perceptrons Classification Intensive Care Units Monitoring

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