_experimental Immunodeficient State Model

Bondarenko, Andriy V.; Pokhil, Sergey I.; Lytvynenko, M. V.; Bocharova, T. V.; Gargin, Vitaliy

doi:10.36740/wlek201909210

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…The linear regression model usage is considered for some main reasons which are being descriptive which support analyzing the association strength among the dependent variable as the output and the inputs as independent variables and adjustment which optimizes covariates effects or the confounders [7]. Also, it supports estimating the major independent factors that influence the dependent variable and analyzes the influence on the dependent variable caused by changing the independent variable per one unit.…”

Section: Linear Regressionmentioning

confidence: 99%

Comparative study of linear regression and SIR models of COVID-19 propagation in Ukraine before vaccination

Mohammadi

Meniailov

Bazilevych

et al. 2021

reks

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The global COVID-19 pandemic began in December 2019 and spread rapidly around the world. Worldwide, more than 230 million people fell ill, 4.75 million cases were fatal. In addition to the threat to health, the pandemic resulted in social problems, an economic crisis and the transition of an ordinary life to a "new reality". Mathematical modeling is an effective tool for controlling the epidemic process of COVID-19 in specified territories. Modeling makes it possible to predict the future dynamics of the epidemic process and to identify the factors that affect the increase in incidence in the greatest way. The simulation results enable public health professionals to take effective evidence-based responses to contain the epidemic. The study aims to develop machine learning and compartment models of COVID-19 epidemic process and to investigate experimental results of simulation. The object of research is COVID-19 epidemic process and its dynamics in territory of Ukraine. The research subjects are methods and models of epidemic process simulation, which include machine learning methods and compartment models. To achieve this aim of the research, we have used machine learning forecasting methods and have built COVID-19 epidemic process linear regression model and COVID-19 epidemic process compartment model. Because of experiments with the developed models, the predictive dynamics of the epidemic process of COVID-19 for 30 days were obtained for confirmed cases, recovered and death. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 1.15, 0.037 and 1.39 percent deviant, respectively, with a linear regression model. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 3.29, 1.08, and 0.71 percent deviant, respectively, for the SIR model. Conclusions. At this stage in the development of the epidemic process of COVID-19, it is more expedient to use a linear model to predict the incidence rate, which has shown higher accuracy and efficiency, the reason for that lies on the fact that the used linear regression model for this research was implemented on merely 30 days (from fifteen days before 2nd of March) and not the whole dataset of COVID-19. Also, it is expected that if we try to forecast in longer time ranges, the linear regression model will lose precision. Alternatively, since SIR model is more comprised in including more factors, the model is expected to perform better in fore-casting longer time ranges.

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Section: Linear Regressionmentioning

confidence: 99%

Comparative study of linear regression and SIR models of COVID-19 propagation in Ukraine before vaccination

Mohammadi

Meniailov

Bazilevych

et al. 2021

reks

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Classification of Diabetes Disease Using Logistic Regression Method

Hrimov

Meniailov

Chumachenko

et al. 2021

Lecture Notes in Networks and Systems

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At the moment, there are many methods of analysis and classification aimed at building the most accurate and effective mathematical models that are widely used in medicine as a decision-making tool. Existing methods make it possible to identify the relationships between input and output variables in the sample, build models reflecting these relationships, compare them in terms of accuracy, profitability and costs, and choose the most effective model. The increase in the incidence of diabetes not only in the world, but also in Ukraine, dictates the need to introduce a mathematical apparatus for automatic diagnosis of the disease. Within the framework of the study, the classification of patients with diabetes by the logistic regression method was implemented. Python is used for software implementation.

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Şəkərli̇ Di̇abet Və Arteri̇al Hi̇pertenzi̇ya Fonunda Mi̇okard İnfarktinin Qulaqciq Fi̇bri̇lyasi̇yasi Sayəsi̇ndə Gi̇zli̇ Keçməsi̇ Təsadüfü

Smiyan,

Romaniuk,

Pristupa

et al. 2023

Azerbaijan Medical Journal

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This article presents information about acute subendocardial myocardial infarction of the posterior wall of the left ventricle, which developed against the background of type 2 diabetes mellitus and arterial hypertension. The only clinical symptom of critical heart disease was a tachysystolic form of permanent atrial fibrillation. Despite a complex of therapeutic manipulations, the patient died. The final diagnosis of acute subendocardial myocardial infarction of the posterior wall of the left ventricle was established only after autopsy. The presented clinical case demonstrates an atypical course of myocardial infarction manifested by latent atrial fibrillation. This case focuses on the need for a comprehensive examination of patients with atrial fibrillation and the exclusion, first of all, of irreversible myocardial damage. Məqalədə sol mədəciyin arxa divarında II tip şəkərli diabet və arterial hipertenziya fonunda törənmiş kəskin subendokardial miokard infarktı təsadüfü haqqında məlumat verilmişdir. Müəlliflərin öz müşahidələrinə əsasən haqqında məlumat verilən xəstədə ürəyin kəskin zədələnməsinin yeganə əlaməti qulaqcıqların uzun sürən taxisistolik formalı fibrillyasiyası olmuşdur. Aparılan kompleks terapevtik müalicə kompleksinə baxmayaraq, xəstə ölmüşdür. Xəstədə kəskin subendokardial miokard infarktı olduğu isə yalnız ölümdən sonra patanatomik təsrih zamanı müəyyənləşdirilmişdir. Məqalədə təsvir edilən klinik hadisə miokard infarktının qulaqcıqların gizli keçən fibrilyasiyası fonunda atipik şəkildə keçə bildiyini nümayiş etdirir. Bu klinik təsadüf qulaqcıq fibrilyasiyası olan pasiyentlərin kompleks şəkildə müayinə edilməli olduğunu və ilk növbədə miokardda baş verə bilən geriyədinməz zədələnmələrin olub-olmadığını araşdırmağın vacibliyini göstərir. В статье представлено сведение об остром субэндокардиальном инфаркте миокарда задней стенки левого желудочка, который развился на фоне сахарного диабета второго типа и артериальной гипертензии. Единственным клиническим симптомом острого поражения сердца была тахисистолическая форма перманентной фибрилляции предсердий. Несмотря на комплекс проведенных терапевтических манипуляций, пациентка умерла. Окончательный диагноз острого субэндокардиального инфаркта миокарда задней стенки левого желудочка был установлен только после вскрытия. Представленный клинический случай демонстрирует нетипическое протекание инфаркта миокарда, который проявлялся скрытой фибрилляцией предсердий. Данный случай акцентирует внимание на необходимость комплексного обследования пациентов при фибрилляции предсердий и исключения в первую очередь необратимых поражений миокарда.

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_experimental Immunodeficient State Model

Cited by 3 publications

References 12 publications

Comparative study of linear regression and SIR models of COVID-19 propagation in Ukraine before vaccination

Comparative study of linear regression and SIR models of COVID-19 propagation in Ukraine before vaccination

Classification of Diabetes Disease Using Logistic Regression Method

Şəkərli̇ Di̇abet Və Arteri̇al Hi̇pertenzi̇ya Fonunda Mi̇okard İnfarktinin Qulaqciq Fi̇bri̇lyasi̇yasi Sayəsi̇ndə Gi̇zli̇ Keçməsi̇ Təsadüfü

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