Кафедра "Інтернет речей"

Постійне посилання колекціїhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/5398

Увага! Поповнення колекції кафедри "Інтернет речей" – призупинено.

Від вересня 2022 року кафедри "Інтернет речей" та "Мультимедійних інформаційних технологій і систем" об’єднані у кафедру "Мультимедійні та інтернет технології і системи".

Первісна назва кафедри – "Розподілені інформаційні системи і хмарні технології".

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An approximate method for constructing linear regression models for prediction of a severe bronchial asthma course
(Institute of Electrical and Electronics Engineers, Inc., USA, 2021) Pihnastyi, O. M.; Kozhyna, O. S.
Diagnosis of a bronchial asthma course in children determines further preventive activities and personalized approaches to treating a child with such pathology. An uncontrolled type of asthma course requires conducting a careful analysis of the factors affecting the formation of severe forms of the disease. The use of linear regression models is a widespread approach that helps to calculate the probability of severe bronchial asthma or the uncontrolled course of the disease development. During this study, 90 children aged from 6 to 18 years old were examined. Of these, there were 70 children suffering from bronchial asthma with different severity and 20 healthy children. The examination included an interviewing of patients as well as a definition of clinical features and results of clinical and laboratory examination in the disease course. 142 factors were analyzed to build a three-parameter model. A correlation ratio and numerical characteristics of model regressors were calculated. Conditions of the use of approximate linear regression models were shown and the model accuracy was estimated. A technique of an approximate model of linear regression building both in dimensional and nondimensional forms was considered. The relationship among model ratios was shown.
Об определении объема бесповторной выборки при проведении медицинских исследований
(Издательский дом "Белгород", 2017) Кожина, Ольга Сергеевна; Пигнастый, Олег Михайлович
Представлен метод определения объема бесповторной выборки при планировании эксперимента.
Choosing the optimal quantity of factors for prediction the severity of bronchial asthma in children using linear regression models
(2021) Pihnastyi, O. M.; Kozhyna, O. S.; Kulik, Tetiana
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.
Methods of the estimated two-parameter linear regression models for diagnosis of bronchial asthma severity in children
(Innovare Academic Sciences, India, 2021) Pihnastyi, O. M.; Kozhyna, O. S.
Objectives: Prognostication of bronchial asthma severity in children by means of two-parameter regression models building. Methods: A clinical study of 70 children with bronchial asthma diagnosis of 6 to 18 years old was done.142 factors were analyzed and a degree of relationship among them was revealed. Single-factor regression models were used during preliminary experimental data processing. Results: The correlation connection between the value observed and the factors under research was revealed. The method of two-parameter linear models with a fair accuracy was developed. Conclusion: The suggested method of approximate two-parameter linear regression models can be used for preliminary analysis of medical research data where the value observed depends on a big number of loosely connected factors.
Use of the complex of models of regression for analysis of the factors that determine the severity of bronchial asthma
(2020) Pihnastyi, O. M.; Kozhyna, Olga S.
Background: According to an International Study of Asthma and Allergies in Childhood (ISAAC), the prevalence of asthma in children of 6-7 years old has increased by 10%, and at the age of 13-14 years by 16% over the last decade. Determining the factors that are keys to the occurrence of the disease and its severity is important in explaining the pathogenesis of bronchial asthma. Methods: Analyzed 142 indicators of clinical and paraclinical examination of 70 children with asthma. To select factors that could be significant in the formation of severe asthma, applied the method of logistic regression with step-by-step inclusion of predictors. Both quantitative and qualitative characteristics were selected. Each qualitative attribute was coded “1” if the child had this characteristic, or “0” if this characteristic had not been established. The formation of a severe asthma course was accepted according to (1) and the absence of a severe asthma flow formation as (0). Results: Analyzed the model of paired regression, the boundary value of thymic stromal lymphopoietin was established, exceeding which indicates the high probability of the presence of severe bronchial asthma. Increasing the value of thymic stromal lymphopoietin by 10 pg/mL suggests an increase in the likelihood of severe asthma by 10%. Conclusions: A complex of steam regression models has been developed to determine the factors characterizing the severity of bronchial asthma. The risk of developing severe bronchial asthma in children has been determined and 15 factors have been identified that affect severe asthma.