Design and application of a smart diagnostic system for parkinson's patients using machine learning

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dc.contributor.authorChanna, Asma.
dc.contributor.authorBaqai, Attiya.
dc.contributor.authorCeylan, Rahime.
dc.date.accessioned2020-03-26T20:13:15Z
dc.date.available2020-03-26T20:13:15Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesi, Mühendislik Fakültesi, Elektirik ve Elektronik Mühendisliği Bölümüen_US
dc.description.abstractFor analysis of Parkinson illness gait disabilities detection is essential. The only motivation behind this examination is to equitably and consequently differentiate among sound subjects and the one who is forbearing the Parkinson, utilizing IOT based indicative framework. In this examination absolute, 16 distinctive force sensors being attached with the shoes of subjects which documented the Multisignal Vertical Ground Reaction Force (VGRF). Overall sensors signals utilizing 1024 window estimate around the raw signals, utilizing the Packet wavelet change (PWT) five diverse characteristics that includes entropy, energy, variance, standard deviation and waveform length were derived and support vector machine (SVM) is to recognize Parkinson patients and healthy subjects. SVM is trained on 85% of the dataset and tested on 15% dataset. Preparation accomplice relies upon 93 patients with idiopathic PD (mean age: 66.3 years; 63% men and 37% ladies), and 73 healthy controls (mean age: 66.3 years; 55% men and 45% ladies). IOT framework included all 16 sensors, from which 8 compel sensors were appended to left side foot of subject and the rest of the 8 on the right side foot. The outcomes demonstrate that fifth sensor worn on a Medial part of the dorsum of right foot highlighted by R5 gives 903% accuracy. Henceforth this examination gives the knowledge to utilize single wearable force sensor. Hence, this examination deduce that a solitary sensor might help in differentiation amongst Parkinson and healthy subjects.en_US
dc.identifier.citationChanna, A., Baqai, A., Ceylan, R. Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning. International Journal of Advanced Computer Science and Applications, 10(6), 563-571.
dc.identifier.endpage571en_US
dc.identifier.issn2158-107Xen_US
dc.identifier.issn2156-5570en_US
dc.identifier.issue6en_US
dc.identifier.pmid#YOKen_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage563en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/37656
dc.identifier.volume10en_US
dc.identifier.wosWOS:000476620800074en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorCeylan, Rahime.
dc.language.isoenen_US
dc.publisherSCIENCE & INFORMATION SAI ORGANIZATION LTDen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectParkinson patientsen_US
dc.subjectforce sensorsen_US
dc.subjectmachine learningen_US
dc.subjectWavelet Packet Transform (WPT)en_US
dc.titleDesign and application of a smart diagnostic system for parkinson's patients using machine learningen_US
dc.typeArticleen_US

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