prikaz prve stranice dokumenta Model strojnog učenja za klasifikaciju poremećaja iz spektra autizma
No public access
undergraduate thesis
Model strojnog učenja za klasifikaciju poremećaja iz spektra autizma
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:483622
Petkoviček, Lucija
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Petkoviček, L. (2024). Model strojnog učenja za klasifikaciju poremećaja iz spektra autizma (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:483622

Petkoviček, Lucija. "Model strojnog učenja za klasifikaciju poremećaja iz spektra autizma." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:483622

Petkoviček, Lucija. "Model strojnog učenja za klasifikaciju poremećaja iz spektra autizma." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:483622

Petkoviček, L. (2024). 'Model strojnog učenja za klasifikaciju poremećaja iz spektra autizma', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 18 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:483622

Petkoviček L. Model strojnog učenja za klasifikaciju poremećaja iz spektra autizma [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 November 18] Available at: https://urn.nsk.hr/urn:nbn:hr:168:483622

L. Petkoviček, "Model strojnog učenja za klasifikaciju poremećaja iz spektra autizma", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:483622

Metadata
TitleModel strojnog učenja za klasifikaciju poremećaja iz spektra autizma
Title (english)Machine learning model for the autism spectrum disorder classification
AuthorLucija Petkoviček
MentorJelena Božek (mentor)
Committee memberJelena Božek (predsjednik povjerenstva)
Committee memberSonja Grgić (član povjerenstva)
Committee memberIvana Žeger (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Poremećaji iz spektra autizma (ASD) smatraju se kompleksnijim izazovom današnje medicine, a primjenom strojnog učenja pokušavaju se otkriti najbolje metode dijagnostike, najčešće korištenjem funkcijskih MRI podataka u stanju mirovanja. ABIDE je inicijativa koja osigurava skup podataka i metode pretprocesiranja. U ovom radu se koristi Preprocessed Connectomes Project CPAC (Configurable Pipeline for the Analysis of Connectomes) metoda pretprocesiranja, ekstrakcija matrice povezanosti pomoću dva atlasa, AAL (Automated Anatomical Labeling) i MIST (Multiresolution Intrinsic Segmentation Template) s više podjela regija, selekcija značajki RFE (Recursive Feature Elimination) metodom i SHAP vrijednostima te klasifikacija metodom potpornih vektora. Uspoređene su točnosti klasifikatora sa i bez selekcije značajki, ovisno o odabiru atlasa. Također su uspoređene i same značajke rangirane kao najbolje za korišteni model klasifikacije ASD, ovisno o metodi selekcije značajki i odabiru atlasa.
Abstract (english)
Autism spectrum disorders (ASD) are considered one of the most complex challenges in modern medicine. By applying machine learning, efforts are being made to discover the best diagnostic methods, often using resting-state functional MRI data. ABIDE is an initiative that provides datasets and preprocessing methods. In this study, Preprocessed Connectomes Project CPAC (Configurable Pipeline for the Analysis of Connectomes) preprocessing method is used, connectivity matrix extraction using two atlases, AAL (Automated Anatomical Labeling) and MIST (Multiresolution Intrinsic Segmentation Template) with multiple region parcelations, feature selection using the RFE (Recursive Feature Elimination) method and SHAP values, and classification using the support vector machine method. The accuracies of the classifiers with and without feature selection are compared, depending on the atlas choice. Additionally, the features ranked as the best for the ASD classification model are compared based on the feature selection method and atlas choice.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:483622
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
Access conditionsClosed access
Terms of useLicence In copyright icon
Public note
Created on2024-10-12 21:50:54