Classification of freezing of gait in Parkinson's disease using machine learning algorithms

Basit Öğe Kaydı
dc.authorid0000-0001-8577-3659
dc.authorid0000-0001-9610-9550
dc.authorid0000-0003-1840-9958
dc.authorscopusid57205613177
dc.authorscopusid57203169526
dc.authorscopusid8945093900
dc.authorscopusid25928587900
dc.contributor.authorÖnder, Mithat
dc.contributor.authorŞentürk, Ümit
dc.contributor.authorPolat, Kemal
dc.contributor.authorPaulraj, D.
dc.date.accessioned2024-09-25T19:42:52Z
dc.date.available2024-09-25T19:42:52Z
dc.date.issued2023
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023 -- 1 November 2023 through 2 November 2023 -- Chennai -- 196097en_US
dc.description.abstractFreezing of gait (FoG) is a prevalent and incapacitating symptom that affects individuals diagnosed with Parkinson's disease (PD) and other movement disorders. Detecting FoG is crucial for accurate diagnosis, fall prevention, and providing objective measurements, all of which are essential for optimizing treatment strategies and improving the quality of life for individuals with FoG. In this study, FoG has been detected using three different classification algorithms: Medium Gaussian Support Vector Machine (SVM), Medium K-Nearest Neighbor (KNN), and Boosted Trees. The process starts with data segmentation, where the dataset is divided into smaller segments. Then, feature extraction is performed on each segment to obtain various statistical measures such as mean, root mean square, maximum, standard deviation, kurtosis, skewness, and peak of root mean square. To ensure a robust and reliable analysis, the dataset is resampled using bootstrapping, a statistical technique that involves drawing random samples from the dataset with replacement. This leads to a more representative sample and reduces the impact of outliers or imbalanced data. The next step is to split the resampled dataset into three different approaches for the classification algorithm: In 5-FCV, the dataset is divided into five equal-sized subsets. Similarly, 10-FCV splits the dataset into ten subsets and follows the same process. Finally, the Medium Gaussian SVM, Medium KNN, and Boosted Trees classification algorithms are applied to the FoG dataset. The classification accuracy achieved is 86.9%, 87.6%, and 92.7% with 10-fold cross-validation, indicating that these algorithms are effective in accurately classifying FoG. © 2023 IEEE.en_US
dc.identifier.doi10.1109/RMKMATE59243.2023.10368706
dc.identifier.endpage5
dc.identifier.isbn979-835030570-8
dc.identifier.scopus2-s2.0-85183565006en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1109/RMKMATE59243.2023.10368706
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12317
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÖnder, Mithat
dc.institutionauthorŞentürk, Ümit
dc.institutionauthorPolat, Kemal
dc.institutionauthorid0000-0001-8577-3659
dc.institutionauthorid0000-0001-9610-9550
dc.institutionauthorid0000-0003-1840-9958
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectAcceleration Signalen_US
dc.subjectBoosted Treesen_US
dc.subjectFreezing of Gait (FoG)en_US
dc.subjectParkinson Diseaseen_US
dc.titleClassification of freezing of gait in Parkinson's disease using machine learning algorithmsen_US
dc.typeConference Objecten_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
mithat-onder.pdf
Boyut:
3.62 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
Bilgisayar Mühendisliği Bölümü Koleksiyonu
Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu