E3S Web Conf.
Volume 166, 2020The International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2020)
|Number of page(s)||6|
|Published online||22 April 2020|
Research of algorithms of Data Mining
Zhytomyr Polytechnic State University, Department of computer science, 103 Chudnivska Str., Zhytomyr, 10005, Ukraine
2 Zhytomyr Polytechnic State University, Department of computer engineering and cybersecurity, 103 Chudnivska Str., Zhytomyr, 10005, Ukraine
3 Zhytomyr Polytechnic State University, Department of software engineering, 103 Chudnivska Str., Zhytomyr, 10005, Ukraine
* Corresponding author: firstname.lastname@example.org
The article explores data mining algorithms, which based on rules and calculations, that allow us to create a model that analyzes the data provided by searching for specific patterns and trends. The purpose of this work is to analyze correlation-regression algorithms on a statistical dataset of chronic diseases. Data mining allows building many models, multiple algorithms can be used within a single solution. The article explores the algorithms of clustering, correlation analysis, Naive Bayes algorithm for obtaining different views of data. Since diabetes is one of the most dangerous chronic diseases, the pathogenesis of which is a lack of insulin in the human body, which causes metabolic disorders and pathological changes in various organs and tissues. As a result, it leads to disability of all functional systems of the body. It was decided to investigate the data related to this disease. Also, the quality of the developed methods of information retrieval from the dataset was evaluated and the most informative features were identified. The developed methods were implemented in the system of intellectual data processing. Past studies show promise of using data mining methods to improve the quality of patient care.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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