A Bayesian Logistic Regression approach in Asthma Persistence Prediction
Background: A number of models based on clinical parameters have been used for the prediction of asthma persistence in children. The number and significance of factors that are used in a proposed model play a cardinal role in prediction accuracy. Different models may lead to different significant...
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| Language: | English |
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Milano University Press
2022-02-01
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| Series: | Epidemiology, Biostatistics and Public Health |
| Online Access: | https://riviste.unimi.it/index.php/ebph/article/view/17433 |
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| author | Ioannis I. Spyroglou Gunter Spöck Eleni A. Chatzimichail Alexandros G. Rigas E.N. Paraskakis |
| author_facet | Ioannis I. Spyroglou Gunter Spöck Eleni A. Chatzimichail Alexandros G. Rigas E.N. Paraskakis |
| author_sort | Ioannis I. Spyroglou |
| collection | DOAJ |
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Background: A number of models based on clinical parameters have been used for the prediction of asthma persistence in children. The number and significance of factors that are used in a proposed model play a cardinal role in prediction accuracy. Different models may lead to different significant variables. In addition, the accuracy of a model in medicine is really important since an accurate prediction of illness persistence may improve prevention and treatment intervention for the children at risk.
Methods: Data from 147 asthmatic children were analyzed by a new method for predicting asthma outcome using Principal Component Analysis (PCA) in combination with a Bayesian logistic regression approach implemented by the Markov Chain Monte Carlo (MCMC). The use of PCA is required due to multicollinearity among the explanatory variables.
Results: This method using the most appropriate models seems to predict asthma with an accuracy of 84.076% and 86.3673%, a Sensitivity of 84.96% and 87.25% and a Specificity of 83.22% and 85.52%, respectively.
Conclusion: Our approach predicts asthma with high accuracy, gives steadier results in terms of positive and negative patients and provides better information about the influence of each factor (demographic, symptoms etc.) in asthma prediction.
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| format | Article |
| id | doaj-art-1d17d954d55d4e00a3881d5c70bf9f36 |
| institution | DOAJ |
| issn | 2282-0930 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | Milano University Press |
| record_format | Article |
| series | Epidemiology, Biostatistics and Public Health |
| spelling | doaj-art-1d17d954d55d4e00a3881d5c70bf9f362025-08-20T03:06:02ZengMilano University PressEpidemiology, Biostatistics and Public Health2282-09302022-02-0115110.2427/12777A Bayesian Logistic Regression approach in Asthma Persistence PredictionIoannis I. Spyroglou0Gunter Spöck1Eleni A. Chatzimichail2Alexandros G. Rigas3E.N. Paraskakis4Democritus University of Thrace, XanthiAdria Universität, KlagenfurtDemocritus University of Thrace, XanthiDemocritus University of Thrace, XanthiDemocritus University of Thrace, Alexandroupolis Background: A number of models based on clinical parameters have been used for the prediction of asthma persistence in children. The number and significance of factors that are used in a proposed model play a cardinal role in prediction accuracy. Different models may lead to different significant variables. In addition, the accuracy of a model in medicine is really important since an accurate prediction of illness persistence may improve prevention and treatment intervention for the children at risk. Methods: Data from 147 asthmatic children were analyzed by a new method for predicting asthma outcome using Principal Component Analysis (PCA) in combination with a Bayesian logistic regression approach implemented by the Markov Chain Monte Carlo (MCMC). The use of PCA is required due to multicollinearity among the explanatory variables. Results: This method using the most appropriate models seems to predict asthma with an accuracy of 84.076% and 86.3673%, a Sensitivity of 84.96% and 87.25% and a Specificity of 83.22% and 85.52%, respectively. Conclusion: Our approach predicts asthma with high accuracy, gives steadier results in terms of positive and negative patients and provides better information about the influence of each factor (demographic, symptoms etc.) in asthma prediction. https://riviste.unimi.it/index.php/ebph/article/view/17433 |
| spellingShingle | Ioannis I. Spyroglou Gunter Spöck Eleni A. Chatzimichail Alexandros G. Rigas E.N. Paraskakis A Bayesian Logistic Regression approach in Asthma Persistence Prediction Epidemiology, Biostatistics and Public Health |
| title | A Bayesian Logistic Regression approach in Asthma Persistence Prediction |
| title_full | A Bayesian Logistic Regression approach in Asthma Persistence Prediction |
| title_fullStr | A Bayesian Logistic Regression approach in Asthma Persistence Prediction |
| title_full_unstemmed | A Bayesian Logistic Regression approach in Asthma Persistence Prediction |
| title_short | A Bayesian Logistic Regression approach in Asthma Persistence Prediction |
| title_sort | bayesian logistic regression approach in asthma persistence prediction |
| url | https://riviste.unimi.it/index.php/ebph/article/view/17433 |
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