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

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.