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undergraduate thesis
Web aplikacija za procjenu rizika obolijevanja i praćenje tijeka bubrežnih bolesti korištenjem strojnog učenja
2024. urn:nbn:hr:200:975282
Adžić, Patricia
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Programming Languages and Systems

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Adžić, P. (2024). Web aplikacija za procjenu rizika obolijevanja i praćenje tijeka bubrežnih bolesti korištenjem strojnog učenja (Undergraduate thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:975282

Adžić, Patricia. "Web aplikacija za procjenu rizika obolijevanja i praćenje tijeka bubrežnih bolesti korištenjem strojnog učenja." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:200:975282

Adžić, Patricia. "Web aplikacija za procjenu rizika obolijevanja i praćenje tijeka bubrežnih bolesti korištenjem strojnog učenja." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:200:975282

Adžić, P. (2024). 'Web aplikacija za procjenu rizika obolijevanja i praćenje tijeka bubrežnih bolesti korištenjem strojnog učenja', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 22 November 2024, https://urn.nsk.hr/urn:nbn:hr:200:975282

Adžić P. Web aplikacija za procjenu rizika obolijevanja i praćenje tijeka bubrežnih bolesti korištenjem strojnog učenja [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2024 [cited 2024 November 22] Available at: https://urn.nsk.hr/urn:nbn:hr:200:975282

P. Adžić, "Web aplikacija za procjenu rizika obolijevanja i praćenje tijeka bubrežnih bolesti korištenjem strojnog učenja", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:200:975282

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TitleWeb aplikacija za procjenu rizika obolijevanja i praćenje tijeka bubrežnih bolesti korištenjem strojnog učenja
Title (english)A web application for renal disease risk assessment and monitoring based on machine learning
AuthorPatricia Adžić
MentorGoran Martinović (mentor)
Committee memberTomislav Galba (predsjednik povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Programming Languages and Systems)
Osijek
Defense date and country2024-09-27, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Program Engineering
Abstract
U završnom radu analizirani su problemi i izazovi u procjeni rizika od bubrežnih bolesti te praćenju njihova tijeka, kao i stanje u tom području i postojeća slična rješenja. Također je bilo važno utvrditi značajke modela i odgovarajuće postupke strojnog učenja prema vrsti problema, pripremiti odgovarajuće skupove podataka za treniranje i testiranje modela, obaviti treniranje i evaluaciju modela strojnog učenja te ga pripremiti za integraciju u web sustav s mogućnošću generiranja preporuka temeljenih na stanju pacijenta. Nadalje, definirani su funkcionalni i nefunkcionalni zahtjevi te model i arhitektura web aplikacije s aspekta klijentske i poslužiteljske strane. Web aplikacija je trebala omogućiti registraciju i prijavu korisnika, izradu osobnog profila, unos i pohranu podataka, procjenu rizika, praćenje bolesti te generiranje i prikaz preporuka pacijentima. Razvoj web aplikacije i modela strojnog učenja proveden je korištenjem prikladnih programskih jezika, tehnologija i razvojnih okvira, a testirani su i analizirani na relevantnim ulaznim podacima i slučajevima korištenja.
Abstract (english)
The final thesis analyzed the problems and challenges in assessing the risk of kidney diseases and monitoring their progression, as well as the state of this field and existing similar solutions. It was also important to determine the characteristics of the model and appropriate machine learning procedures according to the type of problem, prepare suitable datasets for training and testing the model, conduct training and evaluation of the machine learning model, and prepare it for integration into a web system with the capability of generating recommendations based on the patient’s condition. Furthermore, functional and non-functional requirements were defined, along with the model and architecture of the web application from the perspective of the client and server side. The web application was intended to allow user registration and login, creation of a personal profile, data entry and storage, risk assessment, disease monitoring, and the generation and display of recommendations for patients. The development of the web application and machine learning model was carried out using appropriate programming languages, technologies, and development frameworks, and they were tested and analyzed on relevant input data and use cases.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:975282
Study programmeTitle: Undergraduate University Study Programme in Computer Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-10-25 08:55:48