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undergraduate thesis
Detekcija Parkinsonove bolesti iz biomedicinskih parametara snimki glasa
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:980732
Crvelin, Lucia
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Crvelin, L. (2024). Detekcija Parkinsonove bolesti iz biomedicinskih parametara snimki glasa (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:980732

Crvelin, Lucia. "Detekcija Parkinsonove bolesti iz biomedicinskih parametara snimki glasa." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:980732

Crvelin, Lucia. "Detekcija Parkinsonove bolesti iz biomedicinskih parametara snimki glasa." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:980732

Crvelin, L. (2024). 'Detekcija Parkinsonove bolesti iz biomedicinskih parametara snimki glasa', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 28 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:980732

Crvelin L. Detekcija Parkinsonove bolesti iz biomedicinskih parametara snimki glasa [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 December 28] Available at: https://urn.nsk.hr/urn:nbn:hr:168:980732

L. Crvelin, "Detekcija Parkinsonove bolesti iz biomedicinskih parametara snimki glasa", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:980732

Metadata
TitleDetekcija Parkinsonove bolesti iz biomedicinskih parametara snimki glasa
Title (english)Detection of Parkinson's disease based on biomedical voice measurements
AuthorLucia Crvelin
MentorMirjana Domazet-Lošo (mentor)
Committee memberMirjana Domazet-Lošo (predsjednik povjerenstva)
Committee memberMihael Kovač (član povjerenstva)
Committee memberAndrej Slapničar (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-06-28, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Dijagnoza Parkinsonove bolesti često je izazovna i zahtijeva precizne metode. U ovom istraživanju, primijenjene su metode strojnog učenja na biomedicinske parametre iz snimki glasa kako bi se unaprijedila detekcija Parkinsonove bolesti. Korišteni su podaci iz javno dostupne baze te je provedena analiza pomoću algoritama kao što su stroj potpornih vektora, slučajna šuma, k-najbližih susjeda i logistička regresija. Rezultati pokazuju da SVM postiže najbolju točnost od 98.9%, nadmašujući dosadašnje rezultate iz literature. Ovi nalazi sugeriraju da biomedicinski parametri glasa, u kombinaciji s algoritmima strojnog učenja, imaju značajan potencijal za neinvazivnu i ranu dijagnostiku Parkinsonove bolesti, otvarajući mogućnosti za daljnja istraživanja i primjene.
Abstract (english)
Diagnosing Parkinson's disease is often challenging and requires precise methods. In this study, machine learning techniques were applied to biomedical parameters extracted from voice recordings to enhance the detection of Parkinson's disease. Publicly available voice data were used, and the analysis was conducted using algorithms such as Support Vector Machines (SVM), Random Forest, k-Nearest Neighbors (k-NN), and Logistic Regression. The results indicate that SVM achieved the highest accuracy of 98.9%, surpassing previous results reported in the literature. These findings suggest that biomedical voice parameters, combined with machine learning algorithms, hold significant potential for non-invasive and early diagnosis of Parkinson's disease, paving the way for further research and applications.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:980732
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 on2024-10-12 20:18:35