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...
| Main Authors: | , , , |
|---|---|
| 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 |
| Summary: | <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. |
|---|---|
| ISSN: | 1812-2892 2313-1519 |