Using of Artificial Intelligence to Solve the Problem of Cardiovascular Disease Diagnostics

Authors

DOI:

https://doi.org/10.18372/1990-5548.72.16928

Keywords:

artificial intelligence, artificial neural network, cardiovascular diseases, decision trees, deep learning, k-nearest neighbor method, machine learning algorithms

Abstract

The article considers the feasibility of using artificial intelligence, artificial neural networks and machine learning in the tasks of classification and forecasting in the medical field.  The directions in the field of health care in which artificial intelligence was used and the expediency of their use are considered. The analysis of the most frequent diseases among the population is made and the growth rate of diseases is shown. Proof of the success of neural networks when working with cardiovascular diseases, oncology, covid-19. Machine learning algorithms that can be used to create an intelligent system for diagnosing cardiovascular diseases are considered. The characteristics that are advisable to use when creating such a system are presented. The requirements for the creation of an intelligent system that would allow to increase the level of qualification of health care professionals through their interaction with artificial neural networks are formed.

Author Biographies

Olena Chumachenko, National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Doctor of Engineering Science. Professor. Head of the Department.

Department of Artificial Intelligence

Faculty of Informatics and Computer Science

Serhii Kolomoiets , National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

PhD Student

Department of Information Systems

Faculty of Informatics and Computer Science

References

https://ukrstat.gov.ua/operativ/operativ2021/ds/kpops/arh_kpops2021_u.html

https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death

https://niss.gov.ua/sites/default/files/2021-09/ohorona-zdorovya.pdf

http://ekmair.ukma.edu.ua/bitstream/handle/123456789/19375/Hlybovets_Pobudova_diahnostychnoi_ekspertno-medychnoi_systemy.pdf?sequence=1&isAllowed=y

Di. Lin, A. Vasilakos, Yu Tang, and Yuanzhe Yao, “Neural Networks for Computer-Aided Diagnosis in Medicine: A Review,” Neurocomputing, vol. 216, 2016, pp. 700–708. https://doi.org/10.1016/j.neucom.2016.08.039.

Stephen F. Weng, Jenna Reps, Joe Kai, Jonathan M. Garibaldi, and Nadeem Qureshi, “Can machine-learning improve cardiovascular risk prediction using routine clinical data?” PLOS One, April 4, 2017. https://doi.org/10.1371/journal.pone.0174944

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Published

2022-09-23

Issue

Vol. 2 No. 72 (2022)

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES

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