A Bayesian Logistic Regression approach...

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

variables.

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

prediction.