A novel supervised machine learning algorithm to detect Parkinson’s disease on its early stages
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Early and accurate Parkinson’s disease (PD) diagnosis are usually complex as clinical symptoms often onset only when there is extensive loss of dopaminergic neurons in substantia-nigra and symptoms are atypical at an early stages of the disease. Recent brain imaging modality such as single photon emission computed tomography (SPECT) with 123I- Ioflupane (DaTSCAN) have shown to be a better diagnostic tool for PD even in its initial stages. Presently machine learning algorithms have become trendier and play important role to automate PD diagnosis and predict its progression. In machine learning community, support vector regression (SVR) has recently received much attention due to its ability to negotiate between fitting accuracy and model complexity in training prediction models. This work presents an optimized SVR with weights associated to each of the sample data to automate PD diagnosis and predict its progression at primary stages. The proposed algorithm (W-SVR) is trained with motor and cognitive symptom scores in addition to striatal binding ratio (SBR) values calculated from the 123I-Ioflupane SPECT scans (taken from the Parkinson’s progression markers initiative (PPMI) database) for early PD prognosis accurately. In model building, different kernels are used to check the accuracy and goodness of fit. We observed promising results obtained by W-SVR in comparison with classic Support vector regression.