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Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea

BACKGROUND: Diagnosing of obstructive sleep apnea (OSA) is an important subject in medicine. This study aimed to compare the performance of two data mining techniques, support vector machine (SVM), and logistic regression (LR), in diagnosing OSA. The best-fit model was used as a substitute for polys...

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Autores principales: Manoochehri, Zohreh, Salari, Nader, Rezaei, Mansour, Khazaie, Habibolah, Manoochehri, Sara, Pavah, Behnam Khaledi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6091128/
https://www.ncbi.nlm.nih.gov/pubmed/30181747
http://dx.doi.org/10.4103/jrms.JRMS_357_17
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author Manoochehri, Zohreh
Salari, Nader
Rezaei, Mansour
Khazaie, Habibolah
Manoochehri, Sara
Pavah, Behnam Khaledi
author_facet Manoochehri, Zohreh
Salari, Nader
Rezaei, Mansour
Khazaie, Habibolah
Manoochehri, Sara
Pavah, Behnam Khaledi
author_sort Manoochehri, Zohreh
collection PubMed
description BACKGROUND: Diagnosing of obstructive sleep apnea (OSA) is an important subject in medicine. This study aimed to compare the performance of two data mining techniques, support vector machine (SVM), and logistic regression (LR), in diagnosing OSA. The best-fit model was used as a substitute for polysomnography (PSG), which is the gold standard for diagnosing this disease. MATERIALS AND METHODS: A total of 250 patients with sleep problems complaints and whose disease had been diagnosed by PSG and referred to the Sleep Disorders Research Center of Farabi Hospital, Kermanshah, between 2012 and 2015 were recruited in this study. To fit the best LR model, a model was first fitted with all variables and then compared with a model made from the significant variables using Akaike's information criterion (AIC). The SVM model and radial basis function (RBF) kernel, whose parameters had been optimized by genetic algorithm, were used to diagnose OSA. RESULTS: Based on AIC, the best LR model obtained from this study was a model fitted with all variables. The performance of final LR model was compared with SVM model, revealing the accuracy 0.797 versus 0.729, sensitivity 0.714 versus 0.777, and specificity 0.847 vs. 0.702, respectively. CONCLUSION: Both models were found to have an appropriate performance. However, considering accuracy as an important criterion for comparing the performance of models in this domain, it can be argued that SVM could have a better efficiency than LR in diagnosing OSA in patients.
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spelling pubmed-60911282018-09-04 Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea Manoochehri, Zohreh Salari, Nader Rezaei, Mansour Khazaie, Habibolah Manoochehri, Sara Pavah, Behnam Khaledi J Res Med Sci Original Article BACKGROUND: Diagnosing of obstructive sleep apnea (OSA) is an important subject in medicine. This study aimed to compare the performance of two data mining techniques, support vector machine (SVM), and logistic regression (LR), in diagnosing OSA. The best-fit model was used as a substitute for polysomnography (PSG), which is the gold standard for diagnosing this disease. MATERIALS AND METHODS: A total of 250 patients with sleep problems complaints and whose disease had been diagnosed by PSG and referred to the Sleep Disorders Research Center of Farabi Hospital, Kermanshah, between 2012 and 2015 were recruited in this study. To fit the best LR model, a model was first fitted with all variables and then compared with a model made from the significant variables using Akaike's information criterion (AIC). The SVM model and radial basis function (RBF) kernel, whose parameters had been optimized by genetic algorithm, were used to diagnose OSA. RESULTS: Based on AIC, the best LR model obtained from this study was a model fitted with all variables. The performance of final LR model was compared with SVM model, revealing the accuracy 0.797 versus 0.729, sensitivity 0.714 versus 0.777, and specificity 0.847 vs. 0.702, respectively. CONCLUSION: Both models were found to have an appropriate performance. However, considering accuracy as an important criterion for comparing the performance of models in this domain, it can be argued that SVM could have a better efficiency than LR in diagnosing OSA in patients. Medknow Publications & Media Pvt Ltd 2018-07-26 /pmc/articles/PMC6091128/ /pubmed/30181747 http://dx.doi.org/10.4103/jrms.JRMS_357_17 Text en Copyright: © 2018 Journal of Research in Medical Sciences http://creativecommons.org/licenses/by-nc-sa/4.0 This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Original Article
Manoochehri, Zohreh
Salari, Nader
Rezaei, Mansour
Khazaie, Habibolah
Manoochehri, Sara
Pavah, Behnam Khaledi
Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea
title Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea
title_full Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea
title_fullStr Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea
title_full_unstemmed Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea
title_short Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea
title_sort comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6091128/
https://www.ncbi.nlm.nih.gov/pubmed/30181747
http://dx.doi.org/10.4103/jrms.JRMS_357_17
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