Computer-Aided Diagnosis of Parkinson's Disease Using Enhanced Probabilistic Neural Network.
Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In this paper, a comprehensive computer model is pre...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 11; pp. 1 - 13 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925196&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925196 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2015 vid: 39 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925196 115925196 115925196 10.1007/s10916-015-0353-9 115925196 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Computer-Aided Diagnosis of Parkinson's Disease Using Enhanced Probabilistic Neural Network. aug: au: Hirschauer, Thomas Adeli, Hojjat Buford, John affil: Neuroscience Graduate Program and Medical Scientist Training Program, The Ohio State University College of Medicine, Columbus USA sug: subj: Parkinson Disease Diagnosis Neural Networks (Computer) Parkinson Disease Classification Human Record Review Algorithms ROC Curve Health Screening ab: Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In this paper, a comprehensive computer model is presented for the diagnosis of PD based on motor, non-motor, and neuroimaging features using the recently-developed enhanced probabilistic neural network (EPNN). The model is tested for differentiating PD patients from those with scans without evidence of dopaminergic deficit (SWEDDs) using the Parkinson's Progression Markers Initiative (PPMI) database, an observational, multi-center study designed to identify PD biomarkers for diagnosis and disease progression. The results are compared to four other commonly-used machine learning algorithms: the probabilistic neural network (PNN), support vector machine (SVM), k-nearest neighbors (k-NN) algorithm, and classification tree (CT). The EPNN had the highest classification accuracy at 92.5 % followed by the PNN (91.6 %), k-NN (90.8 %) and CT (90.2 %). The EPNN exhibited an accuracy of 98.6 % when classifying healthy control (HC) versus PD, higher than any previous studies. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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