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dc.contributor.authorRasheed, J.
dc.contributor.authorHameed, A.A.
dc.contributor.authorAjlouni, N.
dc.contributor.authorJamil, A.
dc.contributor.authorOzyavas, A.
dc.contributor.authorOrman, Z.
dc.date.accessioned2022-03-04T19:12:27Z
dc.date.available2022-03-04T19:12:27Z
dc.date.issued2020
dc.identifier.isbn9781728196756
dc.identifier.urihttps://doi.org/10.1109/ICDABI51230.2020.9325709
dc.identifier.urihttps://hdl.handle.net/20.500.12436/3202
dc.description2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020 -- 26 October 2020 through 27 October 2020 --en_US
dc.description.abstractParkinson's disease (PD) is a common neurodegenerative disease that has affected millions of people worldwide and is more prevalent in older people. Early detection of this disease remains a challenging task. Recently, vocal and speech data are widely used to detect this disease. In this study, we proposed two classification schemes for improving the identification accuracy of PD cases from voice measurements. First, we applied a variable adaptive moment-based backpropagation algorithm of ANN called BPVAM. Then, we investigated the combination of dimensionality reduction method using principal component analysis (PCA) with BPVAM for classification of the same dataset. The main objective was improving the prediction of PD in the early stages by increasing the sensitivity of the system to dealing with data in its fine detail. In experiments, it was proved that robustness of the system was improved by including features with largest variances using PCA which helped the model to learn the patterns earlier in the training process. Results indicated that BPVAM-PCA was relatively more effective than BPVAM. In addition, these methods were also compared with some other well-known algorithms. © 2020 IEEE.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020en_US
dc.identifier.doi10.1109/ICDABI51230.2020.9325709
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectadaptive neural networksen_US
dc.subjectdiagnosisen_US
dc.subjectParkinson's diseaseen_US
dc.subjectprincipal component analysisen_US
dc.subjectBackpropagationen_US
dc.subjectClassification (of information)en_US
dc.subjectDimensionality reductionen_US
dc.subjectIndustrial economicsen_US
dc.subjectNeural networksen_US
dc.subjectSpeech recognitionen_US
dc.subjectAdaptive back propagationen_US
dc.subjectClassification schemeen_US
dc.subjectDimensionality reduction methoden_US
dc.subjectIdentification accuracyen_US
dc.subjectOlder Peopleen_US
dc.subjectParkinson's diseaseen_US
dc.subjectSpeech dataen_US
dc.subjectTraining processen_US
dc.subjectNeurodegenerative diseasesen_US
dc.titleApplication of Adaptive Back-Propagation Neural Networks for Parkinson's Disease Predictionen_US
dc.typeconferenceObjecten_US
dc.departmentİZÜen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.department-tempRasheed, J., Istanbul Sabahattin Zaim University, Department of Computer Engineering, Istanbul, Turkey; Hameed, A.A., Istanbul University-Cerrahpaşa, Department of Computer Engineering, Istanbul, Turkey; Ajlouni, N., Istanbul Aydin University, Department of Software Engineering, Istanbul, Turkey; Jamil, A., Istanbul Sabahattin Zaim University, Department of Computer Engineering, Istanbul, Turkey; Ozyavas, A., Istanbul Aydin University, Department of Computer Engineering, Istanbul, Turkey; Orman, Z., Istanbul University-Cerrahpaşa, Department of Computer Engineering, Istanbul, Turkeyen_US
dc.authorscopusid57205069065
dc.authorscopusid56338374100
dc.authorscopusid57213339395
dc.authorscopusid49863650600
dc.authorscopusid57219161935
dc.authorscopusid8600333000
dc.identifier.scopus2-s2.0-85100461171en_US


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