20.500.12556/DKUM-98957 Primerjava sodobnih metod strojnega učenja na primeru diagnosticiranja EKG signalov Comparison of modern machine learning methods for ECG signal diagnosis V magistrskem delu primerjamo sodobne pristope strojnega učenja za večoznačno diagnosticiranje 12-odvodnih EKG signalov na podatkovni zbirki Georgia 12-lead ECG Challenge. Posvetimo se modelu random forest ter globokim arhitekturam konvolucijskih in rezidualnih nevronskih mrež, ocenjenim z makro in mikro metrikami, ter razložljivim z metodama SHAP in Grad-CAM. Rezultati kažejo, da ResNet najzanesljiveje prepozna kompleksne vzorce, CNN nudi dobro razmerje med natančnostjo, hitrostjo in velikostjo modela, medtem ko RF služi kot referenca. Kombinacija visoke uspešnosti, dobre razložljivosti in majhne računske zahtevnosti predstavlja ključni korak k zanesljivejši uporabi strojnega učenja v kliničnem okolju. In this master's thesis, we compare modern machine learning approaches for the multi-label diagnosis of 12-lead ECG signals using the Georgia 12-lead ECG Challenge database. We focus on the random forest model and deep architectures of convolutional and residual neural networks, which are evaluated using macro and micro metrics, and explained using SHAP and Grad-CAM methods. The results show that ResNet most reliably recognizes complex patterns, CNN offers a good balance between accuracy, speed, and model size, while RF serves as a reference baseline. The combination of high performance, good explainability, and low computational complexity represents a key step toward the more reliable use of machine learning in a clinical environment. strojno učenje EKG signali medicinska diagnostika klasifikacija metrike machine learning ECG signals medicinal diagnostics classification metrics true true false Slovenski jezik Angleški jezik Magistrsko delo/naloga 2026-07-21 17:06:37 2026-09-24 14:39:48 2026-09-25 03:10:30 0000-00-00 00:00:00 2026 0 Maribor 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 MAG_Zavratnik_Jure_2026.pdf MAG_Zavratnik_Jure_2026.pdf 1 4733704932E6FA394058A65E5D0F70E4 d262aad78097a9e1f4ccaaaab99b358170f24dc771acf03ca74338e42b61743a 51a4b8a3-8517-11f1-9f9e-00155d580e34 https://dk.um.si/Dokument.php?lang=slv&id=208476 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0