Basit öğe kaydını göster

dc.contributor.authorSari, Pelin
dc.contributor.authorBasar, Merve Dogruyol
dc.contributor.authorKilic, Niyazi
dc.contributor.authorAkan, Aydin
dc.date.accessioned2021-03-05T17:20:27Z
dc.date.available2021-03-05T17:20:27Z
dc.identifier.citationBasar M. D. , Sari P., Kilic N., Akan A., "Detection of Chronic Kidney Disease by Using Adaboost Ensemble Learning Approach", 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Türkiye, 16 - 19 Mayıs 2016, ss.773-776
dc.identifier.othervv_1032021
dc.identifier.otherav_c50a8680-83e3-48a4-a8be-0b76ae63f08e
dc.identifier.urihttp://hdl.handle.net/20.500.12627/130682
dc.identifier.urihttps://doi.org/10.1109/siu.2016.7495854
dc.description.abstractChronic kidney disease can be detected with several automatic diagnosis systems. In this study, chronic kidney diseases are diagnosed with Adaboost ensemble learning algorithm. Decision tree based classifiers are used in the diagnosis. The classification performance are evaluated with kappa, mean absolute error (MAE), root mean squared error (RMSE) and area under curve (AUC) criterias. Considering the performance analyses, it is observed that adaboost ensemble learning algorithm provides better classification performance than individual classification.
dc.language.isoeng
dc.subjectSinyal İşleme
dc.subjectMühendislik ve Teknoloji
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectMühendislik
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.titleDetection of Chronic Kidney Disease by Using Adaboost Ensemble Learning Approach
dc.typeBildiri
dc.contributor.departmentİstanbul Üniversitesi , ,
dc.contributor.firstauthorID148383


Bu öğenin dosyaları:

DosyalarBoyutBiçimGöster

Bu öğe ile ilişkili dosya yok.

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster