Title: Early detection of Parkinson's disease by using neuroimaging and biomarkers through hard and soft classifiers

Authors: Gunjan Pahuja; Bhanu Prasad

Addresses: Department of Computer Science and Engineering, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India ' Department of Computer and Information Sciences, Florida A&M University, Tallahassee, Florida 32307, USA

Abstract: Early and accurate detection of Parkinson's disease (PD) remains a challenge. Two prevalent approaches used for the detection of PD are: 1) dopaminergic imaging using single photon emission computed tomography (SPECT) with ¹²³I-Ioflupane; 2) cerebrospinal fluid (CSF) biomarkers. Striatal binding ratio (SBR) values are computed from SPECT and, in this research, it is found that if these SBR values are complemented with CSF biomarkers then these SBR values help increase the accuracy of early PD detection. In this study, SBR values for each of the four striatal regions are complemented with some CSF biomarkers to develop a model for the classification and prediction of early PD. A hard classifier is used for developing the classification submodel, and a soft classifier is used for developing the prediction submodel. The results indicate the effectiveness of the developed model.

Keywords: Parkinson's disease; PD; striatal binding ratio; SBR; hard classifier; soft classifier; multivariate logistic regression; MLR; risk prediction; biomarkers.

DOI: 10.1504/IJMEI.2023.129349

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.2, pp.153 - 165

Received: 01 Dec 2020
Accepted: 28 Feb 2021
Published online: 07 Mar 2023 *

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