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Diabetes is among the socially significant diseases, which leads to high costs for the diagnosis and treatment of diabetes. Diagnosis and treatment of diabetes is currently one of the important tasks in medicine at the present stage of development of medical services. An important direction in the development of medical services for the population is the development and implementation of various problem-oriented information systems. Similar systems developed earlier did not cover the entire amount of heterogeneous information that is collected when diagnosing and prescribing the course of diabetes treatment, nor did they use technologies and cloud services as tools for Big Data. In this article, let’s make use of the predictive analytic to forecast and categorize the type of diabetes which offers an effective method for treating and curing patients at a reduced cost, with improved results such as affordability and availability. An information system platform has been developed and configured to manage the Hadoop cluster, as well as a non-relational database that uses and processes unstructured data in various formats. All experimental research, development of methods and algorithms, as well as solving computational problems were implemented using software languages for application development. The novelty lies in the research of distributed computing models that provide efficient execution of developed algorithms using the conceptual model of the processes of search, extraction and analysis of unstructured data in large data sets. The practical implementation of algorithms was carried out on the basis of methods of object-oriented programming and object-oriented databases, Цукровий діабет належить до соціально значущих захворювань, що призводить до високих витрат на діагностику та лікування цукрового діабету. Діагностика та лікування цукрового діабету в даний час є одним із важливих завдань медицини на сучасному етапі розвитку медичної служби. Важливим напрямом розвитку медичного обслуговування населення є розробка та впровадження різноманітних проблемно-орієнтованих інформаційних систем. Подібні системи, розроблені раніше, не охоплювали весь обсяг різнорідної інформації, яка збирається при діагностиці та призначенні курсу лікування цукрового діабету, а також не використовували технології та хмарні сервіси як інструменти для роботи з великими даними. У цій статті ми використовуємо прогностичну аналітику для прогнозування та класифікації типу діабету, який пропонує ефективний метод лікування та лікування пацієнтів за зниженою ціною з покращеними результатами, такими як доступність та доступність. Розроблено та налаштовано платформу інформаційної системи для управління кластером Hadoop, а також нереляційною базою даних, яка використовує та обробляє неструктуровані дані у різних форматах. Всі експериментальні дослідження, розробка методів та алгоритмів, а також вирішення обчислювальних завдань було реалізовано з використанням мов програмування для розробки додатків. Новизна полягає у дослідженні моделей розподілених обчислень, що забезпечують ефективне виконання розроблених алгоритмів з використанням концептуальної моделі процесів пошуку, вилучення та аналізу неструктурованих даних у великих наборах даних. Практична реалізація алгоритмів здійснювалася на основі методів об'єктно-орієнтованого програмування та об'єктно-орієнтованих баз даних

In the Republic of Kazakhstan, diabetes mellitus is one of the socially significant diseases, which causes a high level of prevalence of this disease among the population, as well as high costs for the diagnosis and treatment of diabetes. Statistical models for predicting the number of patients with diabetes mellitus based on regression analysis using technical and economic indicators in the field of medical services for diabetes have been developed. There was a direct correlation between economic factors and the growth of patients with diabetes in the Republic of Kazakhstan. The analysis of the current state of research on predicting patient growth has shown insufficient elaboration in this subject area. The prevalence of diabetes in the country has the character of an epidemic and leads to large financial costs, thereby increasing the costs of regional budgets for the purchase of insulin and other medicines in the regions.

This study is part of a large study in the field of diabetes diagnosis and treatment support. The system implements functions for storing and processing structured and unstructured data. It is also possible to control the system in automatic and manual modes. In manual management mode, unstructured data is loaded into the developed information system platform using the NoSQLBooster tool. Creating and loading unstructured databases in the MongoDB utility in manual management mode is necessary for data loading. This information system uses the Apache Ambari software product, which allows you to manage the Hadoop cluster.

This paper describes the possibility of developing information system for the support of an endocrinologists for diagnosis and treatment of diabetes on the basis of tools, such as Hadoop, Spark, NoSQL, MapReduce of BigData technology. The developed information system will facilitate the work of endocrinologists so that doctors can use not only their own knowledge, but also the world's knowledge for correct treatment of diabetes. Authors demonstrated initial stage of research work, where the choice of platform is a service of the information system for cloud technology is described.

This paper analyzes the dynamical behavior of a six-bar linkage used in mechanical presses for metal forming such as deep drawing. Raising the technical level of machines requires an expansion of technological capabilities of the equipment and devices of the existing structures, as well as their equipping with fundamentally new mechanisms. In this regard, the task of developing methods for the dynamic of complex flat lever mechanisms with the desired laws of motion of the input and output elements allowing automatizing the implementation of all design phases with the use of computer is quite relevant. The simulation results obtained with a Maple program are validated by comparing the theoretical values of the input moment with the ones obtained from the conservation of energy law. Content of the article is distinguished by serious problem statement, interesting theoretical study and its practical test. This article provides new scientific results that have theoretical and applied significance.

Introduction The prevalence of diabetes in Kazakhstan has reached epidemic proportions, and this disease is becoming a major financial burden. In this research, regression analysis methods were employed to build models for predicting the number of diabetic patients in Kazakhstan in 2019, as this should aid the costing and policy-making performed by medical institutions and governmental offices regarding diabetes prevention and treatment strategies. Methods A brief review of mathematical models that are potentially useful for the task of interest was performed, and the most suitable methods for building predictive models were selected. The chosen models were applied to explore the correlation between population growth and the number of patients with diabetes as well as the correlation between the increase in gross regional product and the growth in the number of patients with diabetes. Moreover, the relationship of population growth and gross domestic product with the growth in the number of patients with diabetes in Kazakhstan was determined. Our research made use of the scikit-learn library for the Python programming language and functions for regression analysis built into the Microsoft Excel software. Results The predictive models indicated that the prevalence of diabetes in Kazakhstan will increase in 2019. Conclusion Mathematical models were used to find patterns in a comprehensive statistical dataset on registered diabetes patients in Kazakhstan over the last 15 years, and these patterns were then used to build models that can accurately predict the prevalence of diabetes in Kazakhstan.