A new outlier detection method based on convex optimization: application to diagnosis of Parkinson's disease

Basit Öğe Kaydı
dc.contributor.authorTaylan, Pakize
dc.contributor.authorYerlikaya-Ozkurt, Fatma
dc.contributor.authorBilgic Ucak, Burcu
dc.contributor.authorWeber, Gerhard-Wilhelm
dc.date.accessioned2024-04-24T16:24:32Z
dc.date.available2024-04-24T16:24:32Z
dc.date.issued2021
dc.departmentDicle Üniversitesien_US
dc.description.abstractNeuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson's disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models.en_US
dc.identifier.doi10.1080/02664763.2020.1864815
dc.identifier.endpage2440en_US
dc.identifier.issn0266-4763
dc.identifier.issn1360-0532
dc.identifier.issue13-15en_US
dc.identifier.pmid35707096
dc.identifier.scopus2-s2.0-85098000337
dc.identifier.scopusqualityQ1
dc.identifier.startpage2421en_US
dc.identifier.urihttps://doi.org/10.1080/02664763.2020.1864815
dc.identifier.urihttps://hdl.handle.net/11468/16754
dc.identifier.volume48en_US
dc.identifier.wosWOS:000601372800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofJournal of Applied Statistics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectNeuroscienceen_US
dc.subjectRegressionen_US
dc.subjectMean-Shift Outliers Modelen_US
dc.subjectM-Estimationen_US
dc.subjectShrinkageen_US
dc.subjectConvex Optimizationen_US
dc.titleA new outlier detection method based on convex optimization: application to diagnosis of Parkinson's diseaseen_US
dc.titleA new outlier detection method based on convex optimization: application to diagnosis of Parkinson's disease
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu