Choosing the optimal quantity of factors for prediction the severity of bronchial asthma in children using linear regression models

Abstract

The severity of the course of bronchial asthma depends on many factors. Clinical and laboratory studies were carried out on 90 children aged 6 to 18. 70 children with bronchial asthma of various degrees of severity as well as 20 healthy school-aged children were included into the main group. 142 predictors were studied, 11 factors were selected from the bottom in accordance with the selection method. Multivariate linear regression models have been developed and analyzed to predict the severity of bronchial asthma disease. The dependence of the forecast quality of the observed value on the number of model regressors is analyzed. The MSE value was used as a characteristic of forecast quality. An estimate of the number of regressors required for a significant increase in the forecast quality is presented. The law of distribution of the error in predicting the severity of bronchial asthma disease in a multifactorial linear regression model has been substantiated. The visual representation of multivariate models is made using the residual plot.