Titel: A Bayesian Logistic Regression approach in Asthma Persistence Prediction

Background: Previous models based on a limited number of clinical parameters that have been used so far failed

to exhibit high accuracy of 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. The aim of this study

is to evaluate a model that could effectively and accurately predict asthma persistence in children.

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


Results: This method using the most appropriate models seems to predict asthma with an accuracy of 84.076%,

84.924%, 86.3673% and 86.1951%, a Sensitivity of 84.96%, 85.49%, 87.25% and 86.38% and a Specificity

of 83.22%, 84.37%, 85.52% and 86.02% 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


Publikationstyp: Beitrag in Zeitschrift (Autorenschaft)
Art der Veröffentlichung Online Publikation
Erschienen in: Epidemiology Biostatistics and Public Health
Epidemiology Biostatistics and Public Health
zur Publikation
 ( PREX S.r.l.; G. Corrao, W. Ricciardi )
Erscheinungsdatum: 01.01.2018
Titel der Serie: -
Bandnummer: 15
Heftnummer: 1
Erstveröffentlichung: Ja
Version: -
Seite: S. 1 - 14


ISSN: 22820930
DOI: -
AC-Nummer: -
Open Access
  • In einem Open-Access-Journal erschienen


Organisation Adresse
Fakultät für Technische Wissenschaften
Institut für Statistik
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
zur Organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee


  • 305907 - Medizinische Statistik
Forschungscluster Kein Forschungscluster ausgewählt
  • Emerging Sources Citation Index (ESCI)
Informationen zum Zitationsindex: Master Journal List
Peer Reviewed
  • Ja
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
  • AG Environmental Monitoring and Risk Evaluation


Organisation Adresse
Democritus University of Thrace
University Campus,
69100 Komotini
University Campus,
GR - 69100  Komotini

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