Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression Model

<b>Background:</b><b> </b>FEV<sub>1</sub>/FVC value is used in the diagnosis of obstructive and restrictive diseases of the lung. It is a parameter reported in the literature that it varies according to lung disease as well as weight, age and gender characteristic...

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Main Authors: Serdar Abut, Fatih Doğanay, Abdullah Yeşilova, Serap Buğa
Format: Article
Language:English
Published: National Scientific Medical Center 2021-06-01
Series:Ķazaķstannyṇ Klinikalyķ Medicinasy
Subjects:
Online Access:https://www.clinmedkaz.org/download/analysis-of-pulmonary-function-test-results-by-using-gaussian-mixture-regression-model-10919.pdf
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author Serdar Abut
Fatih Doğanay
Abdullah Yeşilova
Serap Buğa
author_facet Serdar Abut
Fatih Doğanay
Abdullah Yeşilova
Serap Buğa
author_sort Serdar Abut
collection DOAJ
description <b>Background:</b><b> </b>FEV<sub>1</sub>/FVC value is used in the diagnosis of obstructive and restrictive diseases of the lung. It is a parameter reported in the literature that it varies according to lung disease as well as weight, age and gender characteristics.<i> </i>The aim of this study is to investigate the relationship between age, weight, gender and height characteristics and FEV<sub>1</sub>/FVC value using a heterogeneous population using Gaussian mixture regression method.<br /> <b>Material and Methods: </b>GMR was used to separate the data into components and to make a parameter estimation for each component. The analysis performed on this model revealed that the patients were divided into 5 optimal groups and that these groups showed a regular transition from obstructive pattern to restrictive pattern.<br /> <b>Results: </b>The mean values of the components for FEV1/FVC were found as 50.071 (3.238), 67.034 (1.725), 82.156 (1.329), 93.592 (1.041), 98.466 (0.303), respectively. The effect of the weight on the components in terms of parameter estimation and standard errors of the components was determined as 0.445 (0.129) **, 0.226 (0.053) **, 0.173 (0.053) **, -0.036 (0.026), -0.040 (0.018) *, respectively.<br /> <b>Conclusion: </b>Direct proportional relationship between the patient's weight and the severity of the obstructive pattern, and between the severity of the disease and the age of the patient in both the obstructive and restrictive pattern are expilicitly proved. Furthermore, it has been revealed that data sets containing heterogeneity can be analyzed by dividing them into sub-components using the GMR model.
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spelling doaj.art-f8c3e9760f4748d6bd61235d85d32c582022-12-22T01:39:57ZengNational Scientific Medical CenterĶazaķstannyṇ Klinikalyķ Medicinasy1812-28922313-15192021-06-01183232910.23950/jcmk/10919Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression ModelSerdar Abut0Fatih Doğanay1Abdullah Yeşilova2Serap Buğa3Department of Computer Engineering, Siirt University, Siirt, TurkeyDepartment of Emergency Medicine, Edremit State Hospital, Balıkesir, TurkeyDepartment of Animal Science, Yüzüncü Yıl University, Van, TurkeyPrimary Care Health Center, Balıkesir Provincial Health Directorate, Balıkesir, Turkey<b>Background:</b><b> </b>FEV<sub>1</sub>/FVC value is used in the diagnosis of obstructive and restrictive diseases of the lung. It is a parameter reported in the literature that it varies according to lung disease as well as weight, age and gender characteristics.<i> </i>The aim of this study is to investigate the relationship between age, weight, gender and height characteristics and FEV<sub>1</sub>/FVC value using a heterogeneous population using Gaussian mixture regression method.<br /> <b>Material and Methods: </b>GMR was used to separate the data into components and to make a parameter estimation for each component. The analysis performed on this model revealed that the patients were divided into 5 optimal groups and that these groups showed a regular transition from obstructive pattern to restrictive pattern.<br /> <b>Results: </b>The mean values of the components for FEV1/FVC were found as 50.071 (3.238), 67.034 (1.725), 82.156 (1.329), 93.592 (1.041), 98.466 (0.303), respectively. The effect of the weight on the components in terms of parameter estimation and standard errors of the components was determined as 0.445 (0.129) **, 0.226 (0.053) **, 0.173 (0.053) **, -0.036 (0.026), -0.040 (0.018) *, respectively.<br /> <b>Conclusion: </b>Direct proportional relationship between the patient's weight and the severity of the obstructive pattern, and between the severity of the disease and the age of the patient in both the obstructive and restrictive pattern are expilicitly proved. Furthermore, it has been revealed that data sets containing heterogeneity can be analyzed by dividing them into sub-components using the GMR model.https://www.clinmedkaz.org/download/analysis-of-pulmonary-function-test-results-by-using-gaussian-mixture-regression-model-10919.pdfGaussian mixture regression; pulmonary function test; FEV1/FVC; obstructive pattern; restrictive pattern
spellingShingle Serdar Abut
Fatih Doğanay
Abdullah Yeşilova
Serap Buğa
Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression Model
Ķazaķstannyṇ Klinikalyķ Medicinasy
Gaussian mixture regression; pulmonary function test; FEV1/FVC; obstructive pattern; restrictive pattern
title Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression Model
title_full Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression Model
title_fullStr Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression Model
title_full_unstemmed Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression Model
title_short Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression Model
title_sort analysis of pulmonary function test results by using gaussian mixture regression model
topic Gaussian mixture regression; pulmonary function test; FEV1/FVC; obstructive pattern; restrictive pattern
url https://www.clinmedkaz.org/download/analysis-of-pulmonary-function-test-results-by-using-gaussian-mixture-regression-model-10919.pdf
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AT fatihdoganay analysisofpulmonaryfunctiontestresultsbyusinggaussianmixtureregressionmodel
AT abdullahyesilova analysisofpulmonaryfunctiontestresultsbyusinggaussianmixtureregressionmodel
AT serapbuga analysisofpulmonaryfunctiontestresultsbyusinggaussianmixtureregressionmodel