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undergraduate thesis
Rano otkrivanje zloćudnih novotvorina putem strojnom učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. urn:nbn:hr:168:510565
Bernat, Andrea
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Bernat, A. (2018). Rano otkrivanje zloćudnih novotvorina putem strojnom učenja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:510565

Bernat, Andrea. "Rano otkrivanje zloćudnih novotvorina putem strojnom učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:510565

Bernat, Andrea. "Rano otkrivanje zloćudnih novotvorina putem strojnom učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:510565

Bernat, A. (2018). 'Rano otkrivanje zloćudnih novotvorina putem strojnom učenja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 18 February 2025, https://urn.nsk.hr/urn:nbn:hr:168:510565

Bernat A. Rano otkrivanje zloćudnih novotvorina putem strojnom učenja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2025 February 18] Available at: https://urn.nsk.hr/urn:nbn:hr:168:510565

A. Bernat, "Rano otkrivanje zloćudnih novotvorina putem strojnom učenja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:510565

Metadata
TitleRano otkrivanje zloćudnih novotvorina putem strojnom učenja
Title (english)Machine Learning for Cancer Detection
AuthorAndrea Bernat
MentorVladimir Čeperić (mentor)
Committee memberVladimir Čeperić (predsjednik povjerenstva)
Committee memberIvan Krešo (član povjerenstva)
Committee memberPetra Bevandić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad predstavlja primjenu različitih algoritama strojnog učenja u dijagnostici raka dojke. Svi navedeni algoritmi strojnog učenja pokazali su visoke performanse na binarnoj klasifikaciji raka dojke, tj. određivanje da li je benigni tumor ili maligni tumor. Susreli smo se i s pojmom prenaučenosti modela koji se pokazuje kao najčešći problem strojnog učenja. Da bismo izbjegli taj problem treba koristiti tehniku unakrsne provjere kao što je k-spremnika unakrsna provjera. Primjena takve tehnike neće samo pružiti točniju mjeru izvedbe predviđanja modela, nego će također pomoći u određivanju najoptimalnijih hiperparametra algoritama strojnog učenja.
Abstract (english)
This paper presents an application of different machine learning algorithms for the diagnosis of breast cancer.All presented Machine Learning algorithms exhibited high performance on the binary classification of breastcancer,i.e. determining whether benign tumor or malignant tumor. We have also come up with the concept of overfitting of the model that appears to be the most common problem of machine learning. To avoid this problem we should use a cross validation such as a k-fold cross validation.The application of such a technique will not only provide a more accurate measure of model prediction performance,but it will also assist in determining the most optimal hyper-parameters for the Machine Learning algorithms
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:510565
Study programmeTitle: Computing
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
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Created on2019-03-13 19:02:36