Analiza urinarnih biomarkera u svrhu predviđanja raka gušterače

undergraduate thesis

• Title (english): Urinary biomarkers for detection of pancreatic cancer
• Author: Idora Ban
• Mentor: Mirjana Domazet-Lošo (mentor)
• Committee member: Mirjana Domazet-Lošo (predsjednik povjerenstva)
• Committee member: Mario Brčić (član povjerenstva)
• Committee member: Mihael Kovač (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2023-06-30, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Karcinom gušterače izrazito je smrtonosna vrsta raka čijoj niskoj stopi preživljavanja najviše pridonosi činjenica da se simptomi počinju pojavljivati tek kada je bolest u uznapredovalom stadiju. U okviru ovoga rada proučavan je javno dostupni skup podataka koji uključuje vrijednosti urinarnih biomarkera kod zdravih osoba i osoba oboljelih od raka gušterače. Odabrane su i primijenjene metode strojnog učenja koje bi na temelju navedenog skupa mogle predvidjeti rak gušterače ili omogućiti dijagnosticiranje u ranijem stupnju bolesti. Najbolji rezultati dobiveni su korištenjem algoritama slučajna šuma, gradient boosting i stroj potpornih vektora. Implementirana je binarna i višeklasna klasifikacija, a evaluirani modeli uspoređivani su međusobno i s već postojećim analizama.

Abstract (english)

Pancreatic cancer is an extremely deadly type of cancer whose low survival rate is attributable to the fact that symptoms only begin to appear when the disease has reached an advanced stage. In this thesis, a publicly available data set was studied, consisting of the values of urinary biomarkers in healthy people and patients with pancreatic cancer. Multiple machine learning methods were chosen and applied, assuming that they can use the aforementioned data set to predict pancreatic cancer or make earlier detection possible. The best results were obtained by using random forest, gradient boosting and support vector machine algorithms. Both binary and multiclass classification was implemented and the evaluated models were compared with each other and with existing analyses.

• Keywords: rak gušterače; biomarkeri; strojno učenje; slučajna šume; gradient boosting; stroj potpornih vektora; klasifikacija
• Keywords (english): pancreatic cancer; biomarkers; machine learning; random forest; gradient boosting; support vector machine; classification
• Language: croatian
• URN:NBN: urn:nbn:hr:168:784211
• Study programme: Title: 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 resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-07-13 09:54:22