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Computer-Aided Diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network

  • Patient Facing Systems
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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.

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Acknowledgments

PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including AbbVie, Avid, Biogen, Bristol-Myers Squibb, Covance, GE Healthcare, Genentech, GlaxoSmithKline, Eli Lilly and Company, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal Imaging, Roche, Servier, and UCB.

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Authors and Affiliations

  1. Neuroscience Graduate Program and Medical Scientist Training Program, The Ohio State University College of Medicine, Columbus, OH, USA

    Thomas J. Hirschauer

  2. Departments of Biomedical Engineering, Biomedical Informatics, Neurology, Neuroscience, Electrical and Computer Engineering, Civil, Environmental, and Geodetic Engineering, and Biophysics Graduate Program, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43210, USA

    Hojjat Adeli

  3. Physical Therapy Division, School of Health and Rehabilitation Sciences, The Ohio State University, 453 W 10th Ave, Rm. 516E, Columbus, OH, 43210, USA

    John A. Buford

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  1. Thomas J. Hirschauer
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Correspondence to Hojjat Adeli.

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Hirschauer, T.J., Adeli, H. & Buford, J.A. Computer-Aided Diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network. J Med Syst 39, 179 (2015). https://doi.org/10.1007/s10916-015-0353-9

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