Paper Info
Title
Computer-Aided Diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network
Abstract
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.
Year
DOI
Venue
2015
10.1007/s10916-015-0353-9
J. Medical Systems
Keywords
Field
DocType
Computer-aided diagnosis, Parkinson’s disease, Enhanced probabilistic neural networks
Parkinson's disease,Support vector machine,Computer-aided diagnosis,Probabilistic neural network,Biomarker (medicine),Artificial intelligence,Neuroimaging,Medicine,Medical diagnosis,Machine learning,Decision tree learning
Journal
Volume
Issue
ISSN
39
11
1573-689X
Citations 
PageRank 
References 
43
0.99
23
Authors
3
Name
Order
Citations
PageRank
thomas j hirschauer1430.99
Hojjat Adeli22150148.37
john a buford3431.66