1. ELARTU — Інституційний репозитарій ТНТУ імені Івана Пулюя
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  4. 124 — системний аналіз
Ezzel az azonosítóval hivatkozhat erre a dokumentumra forrásmegjelölésben vagy hiperhivatkozás esetén: http://elartu.tntu.edu.ua/handle/lib/48075
Összes dokumentumadat
DC mezőÉrtékNyelv
dc.contributor.advisorЗолотий, Роман Захарійович-
dc.contributor.advisorZolotyi, Roman Z.-
dc.contributor.authorNaadu, Ali Zulaikha-
dc.date.accessioned2025-01-30T16:03:14Z-
dc.date.available2025-01-30T16:03:14Z-
dc.date.issued2025-01-30-
dc.date.submitted2025-01-16-
dc.identifier.citationNaadu A. Z Heart Disease Prediction Using Machine Learning Alghorithms : work towards a master's degree : spec. 124 - system analysis / supervisor R. Z. Zolotyi. Ternopil : Ternopil Ivan Puluj National Technical University, 2025. 75 p.uk_UA
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/48075-
dc.description.abstractDuring the course of the thesis, a fully functional software for generating crosswords was obtained, which runs on the Windows operating system. The interface of the created program is convenient, simple, and intuitive improves its capabilities The developed application is a tool for interactively creating crossword forms and filling them in. In other words, it is a crossword generator. Similar applications can be used by various publications that either specialize in publishing crosswords or sometimes decorate their products with them. The developed software satisfies all the requirements set at the task formulation stage.uk_UA
dc.description.tableofcontentsINTRODUCTION 6 1. ANALYSIS OF DEVELOPMENTS ON THE TOPIC OF THE WORK 8 1.1. Justification of the relevance of the chosen topic 8 1.2. Areas of application 10 1.3. Justification of the feasibility of improving existing solutions 12 1.4. Problem statement 13 2. PRINCIPLES OF MACHINE LEARNING AND DATA PROCESSING ALGORITHMS 15 2.1. History of machine learning 15 2.2. Application areas and frameworks 19 2.3. Working principles and methods 23 3. DEVELOPMENT OF PROGRAM STRUCTURE AND ALGORITHMS 32 3.1. Model 32 3.2. Materials and methods 36 3.3. Results of work 43 3.4. Software 50 3.5. Auxiliary devices 51 3.6. Work scheme 60 4 SAFETY OF LIFE, BASIC LABOR PROTECTION 64 4.1. Effects of electromagnetic radiation on the human body 64 4.2 Types of hazards 67 4.3 Road Transport Safety 70 4.4 Conclusions 70 CONCLUSIONS 72 REFERNCES 73uk_UA
dc.format.extent75-
dc.language.isoukuk_UA
dc.publisherТернопільський національний технічний університет імені Івана Пулюяuk_UA
dc.subjectsystem analysisuk_UA
dc.subjectsszuk_UA
dc.subjectlearninguk_UA
dc.subjectpytorchuk_UA
dc.subjectprogramuk_UA
dc.subjectcodeuk_UA
dc.titleHeart Disease Prediction Using Machine Learning Alghorithmsuk_UA
dc.typeMaster Thesisuk_UA
dc.rights.holder© Ali Zulaikha Naadu, 2025uk_UA
dc.coverage.placenameТНТУ ім. І.Пулюя, ФІС, м. Тернопіль, Українаuk_UA
dc.subject.udc004.04uk_UA
dc.relation.references1. Mancia, S. Oparil, PK Whelton, M. McKee, A. Dominiczak, FC Lu ft, K. AlHabib, F. Lanas, A. Damasceno, D. Prabhakaran, G. La Torre The technical report on sodium intake and cardiovascular disease in low-and middle-income countries by the joint working group of the World Heart Federation, the European Society of Hypertension and the European Public Health Association Eur Heart J, 38 [Text], (10) (2017). 712-719 p.uk_UA
dc.relation.references2. AND. Coronary Heart disease [Text], (2020), 468 p.uk_UA
dc.relation.references3. AND. D'Souza To predict cardiac disease, data mining techniques are applied 74-77 Int. J.Res.Eng.Sci., 3 (3) [Text], (2015) 75 p.uk_UA
dc.relation.references4. Sanjiv, JS (2015), [Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction], Circulation, #131, [Text], pp. 269–279.uk_UA
dc.relation.references5. Choi E. (2017) [Using recurrent neural network models for early detection of heart failure onset], Journal of the American Medical Informatics Association, No. 24, [Text], pp. 361–370.uk_UA
dc.relation.references6. Koelio LP, Richard V. (2016) [Construction of machine learning systems in Python], DMK Publishing House, [Text], 303 p.uk_UA
dc.relation.references7. WJ Loesche Periodontal disease as a risk factor for heart disease Compendium, 15 (8) [Text], (1994), 976-978 p.uk_UA
dc.relation.references8. SK Sen Using machine learning methods in heart disease detection and prediction Int. J.Eng.Comput. Sci., 6 (6) [Text], (2017), pp. 21623-21631.uk_UA
dc.relation.references9. R. Hertel, R. Benlamri A deep learning segmentation-classification pipeline for x-ray-based covid-19 diagnosis” Biomedical Engineering Advances (2022), Article 100041.uk_UA
dc.relation.references10. Chattopadhyay, M. Maitra MRI-Based brain tumor image detection using CNN based deep learning method Neurosci.Inf. (2022), Article 100060.uk_UA
dc.relation.references11. SK Mamatha, HK Krishnappa, N. Shalini Graph theory based segmentation of magnetic resonance images for brain tumor detection Pattern Recogn Image Anal, 32 (1) [Text], (2022), pp. 153-161.uk_UA
dc.relation.references12. GN Ahmad, H. Fatima, AS Saidi Efficient medical diagnosis of human heart diseases using machine learning techniques with and without GridSearchCV IEEE Access [Text], (2022), 576-596 p.uk_UA
dc.relation.references13. MM Rahman, MR Rana, Nur-A-Alam, MSI Khan, KMM Uddin A web-based heart disease prediction system using machine learning algorithms Netw. Biol., 12 (2) [Text], (2022), pp. 64-81.uk_UA
dc.relation.references14. SK Dey, MM Rahman, A. Howlader, UR Siddiqi, KMM Uddin, R. Borhan, EU Rahman Prediction of dengue incidents using hospitalized patients, metrological and socio-economic data in Bangladesh: a machine learning approach PLoS One, 17 (7) (2022), Article e0270933.uk_UA
dc.relation.references15. DVB Oliveira, JF da Silva, TA de Sousa Araújo, UP Albuquerque Influence of religiosity and spirituality on the adoption of behaviors of epidemiological relevance in emerging and re-emerging diseases: the case of dengue fever J Relig Health, 61 (1) [Text], (2022), pp. 564-585.uk_UA
dc.relation.references16. Vrakina K. P. Proceedings of XII International Scientific and Practical Conference. 2022. C. 161-164. URL: EURASIAN-SCIENTIFIC-DISCUSSIONS-18-20.12.2022.pdf (sci-conf.com.ua).uk_UA
dc.relation.references17. Y. Leshchyshyn, L. Scherbak, O. Nazarevych, V. Gotovych, P. Tymkiv and G. Shymchuk, «Multicomponent Model of the Heart Rate Variability Change-point,» 2019 IEEE XVth International Conference on the Perspective Technologies and Methods in MEMS Design (MEMSTECH), Polyana, Ukraine, 2019, pp. 110-113, doi: 10.1109/MEMSTECH.2019.8817379.uk_UA
dc.relation.references18. Lytvynenko, S. Lupenko, O. Nazarevych, G. Shymchuk and V. Hotovych, «Mathematical model of gas consumption process in the form of cyclic random process,» 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), LVIV, Ukraine, 2021, pp. 232-235, doi: 10.1109/CSIT52700.2021.9648621.uk_UA
dc.relation.references19. V. Kozlovskyi, Y. Balanyuk, H. Martyniuk, O. Nazarevych, L. Scherbak and G. Shymchuk, «Information Technology for Estimating City Gas Consumption During the Year,» 2022 International Conference on Smart Information Systems and Technologies (SIST), Nur-Sultan, Kazakhstan, 2022, pp. 1-4, doi: 10.1109/SIST54437.2022.9945786.uk_UA
dc.relation.references20. I. Lytvynenko, S. Lupenko, N. Kunanets, O. Nazarevych, G. Shymchuk and V. Hotovych, "Simulation of gas consumption process based on the mathematical model in the form of cyclic random process considering the scale factors", 1st International Workshop on Information Technologies: Theoretical and Applied Problems ITTAP 2021, 16–18 November 2021.uk_UA
dc.relation.references21. Approach to gas consumption process forecasting on the basis of a mathematical model in the form of a random cyclic process / Serhii Lupenko, Iaroslav Lytvynenko, Oleg Nazarevych, Grigorii Shymchuk, Volodymyr Hotovych // ICAAEIT 2021, 15-17 December 2021. – Tern. : TNTU, Zhytomyr «Publishing house „Book-Druk“» LLC, 2021. – P. 213–219. – (Mathematical modeling in power engineering and information technologies).uk_UA
dc.relation.references22. Конспект лекцій з дисципліни «Грід-системи та технології хмарних обчислень» для студентів освітніх рівнів «бакалавр», «магістр» / Укладачі : Шимчук Г.В., Маєвський О.В., Назаревич О.Б., Стадник М.А. – Тернопіль : Тернопільський національний технічний університет імені Івана Пулюя, 2016 – 340 с.uk_UA
dc.contributor.affiliationТернопільський національний технічний університет імені Івана Пулюя, факультет комп’ютерно-інформаційних систем і програмної інженерії, кафедра комп’ютерних наук, м. Тернопіль, Українаuk_UA
dc.coverage.countryUAuk_UA
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