Paterega Y. Information Technology for Processing Personalized Data for Analyzing the State of a Person

Українська версія

Thesis for the degree of Candidate of Sciences (CSc)

State registration number

0424U000265

Thesis Registration Form

0424U000265.pdf

Applicant for

Specialization

  • 05.13.06 - Інформаційні технології

04-10-2024

Specialized Academic Board

Д35.052.14

Lviv Polytechnic National University

Essay

In the dissertation, an important scientific problem has been solved — the process of personalized data processing has been improved by enhancing classification accuracy and reducing the number of iterations in the machine learning process by applying augmentation to the training dataset. A generalized model of information technology for processing personalized data to analyze an individual’s condition has been developed by consolidating multimodal data, which enables improving the accuracy and completeness of the disease stage identification process and supports decision-making for effective treatment. A method for improving the accuracy of personalized medical data classification has been developed by introducing a data augmentation stage during the processing of medical information about an individual’s condition. The method of personalizing individual data has been improved, which, unlike existing methods, utilizes an ensemble of classification models and ensemble voting, allowing for an increase in the accuracy of predicting an individual's condition. An algorithm for processing personalized medical data of an individual has been developed to analyze their condition, enabling the formalization of the data preparation process for patients with various pathologies. The architecture of an information system for processing personalized data has been developed, based on which an applied information system for processing personalized data to analyze an individual's condition has been implemented.

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Research papers

1. Melnykova N., Paterega Iu. Imbalanced data: a comparative analysis of classification enhancements using augmented data. Intellektuelles Kapital–die Grundlage für innovative Entwicklung: Innovative Technologie, Informatik, Sicherheitssysteme, Verkehrsentwicklung, Physik und Mathematik. Monografische Reihe «Europäische Wissenschaft». Buch 28. Teil 3 = Intellectual capital is the foundation of innovative development: Innovative technology, Computer science, Security systems, Transport development, Physics and mathematics, Agriculture. Monographic series «European Science». Book 28. Part 3 : monograph. Karlsruhe: ScientificWorld-NetAkhatAV, 2024. P. 54–72. DOI: 10.30890/2709-2313.2024-28-00-01.

2. Bokhonko А., Melnykova N., Patereha Yu. Comparative analysis of data augmentation methods for image modality. Вісник Тернопільського національного технічного університету. 2024. № 1 (113). С. 16–26. / https://visnyk.tntu.edu.ua/index.php?art=762.

3. Patereha Yu., Мelnyk М. Prediction of the occurrence of stroke based on machine learning models. Комп'ютерні системи проектування. Теорія і практика. 2024. Вип. 6, № 1. С. 17–27. https://doi.org/10.23939/cds2024.01.017

4. Paterega I. Main strategies for autonomous robotic controller design. Радіоелектроніка та інформатика. 2011. Вип. 4. С. 36–41. https://openarchive.nure.ua/entities/publication/505fca9b-c6b2-445c-9c23-e4338981c56d.

5. Патерега Ю. І. Особливості використання штучних нейронних осциляторів у робототехніці. Науковий вісник НЛТУ України. 2010. Вип. 20.13. С. 322–331.

6. Тимощук П. В., Патерега Ю. І. Штучні нейронні осцилятори. Вісник Національного університету "Львівська політехніка". Серія: Комп’ютерні системи проектування. Теорія і практика. 2009. № 651. С. 40–45.

7. Nykoniuk M., Melnykova N., Patereha Yu., Sala D., Cichoń D. Classification of patients with the development of Alzheimer's disease using an ensemble of machine learning models. CEUR Workshop Proceedings. 2023. Vol. 3609 : 6th Intern. conf. on informatics and data-driven medicine IDDM 2023, Bratislava, Slovakia, 17-19 Nov. 2023. P. 198–216. (Scopus) DOI: 10.30890/2709-2313.2024-28-00-017. / https://ceur-ws.org/Vol-3609/short4.pdf

8. Paterega Yu. I. Analysis of neural network controller for mobile robot navigation / // САПР у проектуванні машин. Питання впровадження та навчання : матеріали XVIII Міжнар. укр.-пол. наук.-техн. конф. CADMD'2010, 14–16 жовт. 2010, Львів, Україна / Нац. ун-т «Львів. політехніка». – Л.: Вежа і Ко, 2010. – C. 91–92.

9. Paterega Yu. Artificial neural oscillators in robotics. Perspective Technologies and Methods in MEMS Design MEMSTECH'2010 : proc. of the 6th Intern. conf., 20-23 Apr. 2010. P. 123– 130. (Scopus).

10. Paterega Yu. Izhikelich's model of spiking neurons // Computer science and information technologies : proc. of the V Intern. sci. and techn. conf. CSIT 2010, 14–16 Oct. 2010, Lviv, Ukraine / Lviv Polytechnic Nat. Univ. – Lviv : Publ. House Vezha and Co, 2010. – P. 32–33.

11. Tymoshchuk P. V., Paterega Yu. I. Mathematical models of spiking neurons // Computer science and information technologies : proc. of the V Intern. sci. and techn. conf. CSIT 2010, 14–16 Oct. 2010, Lviv, Ukraine / Lviv Polytechnic Nat. Univ. – Lviv : Publ. House Vezha and Co, 2010. – P. 47–48.

12. Tymoshchuk P. V., Paterega Y. I. Implementation of artificial neural oscillators. 5th Intern. Conf. on Perspective Technologies and Methods in MEMS Design MEMSTECH 2009, 22-24 Apr. 2009. Р. 149–154 (Scopus).

13. Paterega I. Artificial evolution mechanisms in robot navigation. 2011 11th International Conference “The Experience of Designing and Application of CAD Systems in Microelectronics” CADSM 2011, 23-25 Febr. 2011. P. 281–286 (Scopus).

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