Choosing the optimal quantity of factors for prediction the severity of bronchial asthma in children using linear regression models
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Дата
2021
DOI
Науковий ступінь
Рівень дисертації
Шифр та назва спеціальності
Рада захисту
Установа захисту
Науковий керівник
Члени комітету
Видавець
Анотація
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.
Опис
Ключові слова
Bronchial asthma, child, severe asthma, prediction, MSE, regression model, residual plot
Бібліографічний опис
Pihnastyi O. Choosing the optimal quantity of factors for prediction the severity of bronchial asthma in children using linear regression models [Electronic resource] / O. Pihnastyi, O. Kozhyna, T. Kulik // CEUR Workshop Proceedings. – 2021. – Vol. 3039. – Information Technologies: Theoretical and Applied Problems (ITTAP 2021) : proc. of the 1st Intern. Workshop,Ternopil, Ukraine, November 16-18, 2021 / ed.: Ia. Lytvynenko, S. Lupenko ; Ternopil Ivan Puluj Nation. Technic. Univ. – Electronic text data. – Ternopil, 2021. – P. 82-96. – URL: http://ceur-ws.org/Vol-3039/paper21.pdf, free (accessed 11.01.2022).