Feature Selection For Classification Based On Fine Motor Signs Of Parkinson'S Disease

Effective evaluation of potential neuroprotective interventions for Parkinson's disease (PD) requires precise quantification of the motor signs associated with this disease. We have created a protocol that uses force tracking in a simultaneous task paradigm to quantify the tine motor control deficits in individuals with PD. We have used this protocol to collect data from 30 individuals with early to moderate PD and 30 age-matched controls. Based on this data, we computed 60 variables. We generated all possible combinations of three of these variables, and we then computed the classification accuracy of a support vector machine (SVM) trained on each variable combination. We were able to correctly classify 85% of subjects as with or without PD. We found that root-mean-square error variables were the most important features for classification and that utilizing a simultaneous task paradigm improves classification accuracy.