prikaz prve stranice dokumenta primjena strojnog učenja u dijagnostici raka dojke
Access restricted to students and staff of home institution
undergraduate thesis
primjena strojnog učenja u dijagnostici raka dojke
2022. urn:nbn:hr:155:461780
Bojanić, Katarina
University of Dubrovnik
Faculty of Electrical Engineering and Applied Computing

Cite this document

Bojanić, K. (2022). primjena strojnog učenja u dijagnostici raka dojke (Undergraduate thesis). Dubrovnik: University of Dubrovnik. Retrieved from https://urn.nsk.hr/urn:nbn:hr:155:461780

Bojanić, Katarina. "primjena strojnog učenja u dijagnostici raka dojke." Undergraduate thesis, University of Dubrovnik, 2022. https://urn.nsk.hr/urn:nbn:hr:155:461780

Bojanić, Katarina. "primjena strojnog učenja u dijagnostici raka dojke." Undergraduate thesis, University of Dubrovnik, 2022. https://urn.nsk.hr/urn:nbn:hr:155:461780

Bojanić, K. (2022). 'primjena strojnog učenja u dijagnostici raka dojke', Undergraduate thesis, University of Dubrovnik, accessed 16 December 2024, https://urn.nsk.hr/urn:nbn:hr:155:461780

Bojanić K. primjena strojnog učenja u dijagnostici raka dojke [Undergraduate thesis]. Dubrovnik: University of Dubrovnik; 2022 [cited 2024 December 16] Available at: https://urn.nsk.hr/urn:nbn:hr:155:461780

K. Bojanić, "primjena strojnog učenja u dijagnostici raka dojke", Undergraduate thesis, University of Dubrovnik, Dubrovnik, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:155:461780

Please login to the repository to save this object to your list.
Metadata
Titleprimjena strojnog učenja u dijagnostici raka dojke
AuthorKatarina Bojanić
MentorMario Miličević (mentor)
Committee memberKrunoslav Žubrinić (predsjednik povjerenstva)
Committee memberMario Miličević (član povjerenstva)
Committee memberAna Kešelj (član povjerenstva)
GranterUniversity of Dubrovnik
(Faculty of Electrical Engineering and Applied Computing)
Dubrovnik
Defense date and country2022-09-22, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad se bavi temom primjene strojnog učenja u medicinskoj grani onkologije u svrhu automatizirane dijagnostike pacijenata. U onkologiji se posebna važnost pridaje otkrivanju bolesti u ranoj fazi i postavljanju što točnije dijagnoze. U teorijskom dijelu rada opisani su problemi u onkološkoj dijagnostici s posebnim naglaskom na rak dojke te je dan pregled istraživanja i napretka u području ostvarenog koristeći strojno učenje. Uvedeni su osnovni pojmovi iz domene strojnog učenja i opisan princip rada dva algoritma učenja, SGD i SMO. U empirijskom dijelu rada navedeni algoritmi su primijenjeni na skup podataka o raku dojke koji sadrži informacije o vrstama i svojstvima tumora. Podaci su prethodno obrađeni i analizirani pomoću softverskog alata Weka te su u konačnici izneseni ostvareni rezultati. Rezultati na podacima korištenim u ovom radu pokazuju zadovoljavajuću razinu točnosti kod određivanja vrste tumora.
Abstract (english)
This paper deals with the topic of application of machine learning in the medical branch of oncology for the purpose of automated patient diagnosis. In oncology, special importance is attached to detecting the disease at an early stage and making the diagnosis as accurate as possible. In the theoretical part of the paper, problems in oncological diagnostics are described with special emphasis on breast cancer, and an overview of research and progress in the field achieved using machine learning is given. Basic concepts from the field of machine learning are introduced and the working principle of two learning algorithms, SGD and SMO, is described. In the empirical part of the work, the mentioned algorithms were applied to the breast cancer data set, which contains information on the types and properties of tumors. The data were pre-processed and analyzed using the Weka software tool, and finally the achieved results were presented. The results on the data used in this work show a satisfactory level of accuracy in determining the type of tumor.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:155:461780
Study programmeTitle: Applied Computer Science / Information Management
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 conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2022-10-04 08:55:42