Automatic classification and prediction models for early Parkinson%26apos;s disease diagnosis from SPECT imaging

作者：Prashanth R^*; Roy Sumantra Dutta; Mandal Pravat K; Ghosh Shantanu

来源：Expert Systems with Applications, 2014, 41(7): 3333-3342.

DOI：10.1016/j.eswa.2013.11.031

• Computer-aided early diagnosis
• Parkinson%26apos
• s disease
• Risk prediction
• Pattern analysis
• Support vector machine
• Logistic regression

Early and accurate diagnosis of Parkinson%26apos;s disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with I-123-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the I-123-loflupane SPECT scans (as obtained from the Parkinson%26apos;s progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.

• 出版日期2014-6-1