Computational approaches for understanding the diagnosis and treatment of Parkinson's disease


This study describes how the appliance of evolutionary algorithms (EAs) will be used to review motor operate in humans with Parkinson's disease (PD) and in animal models of PD. Human information is obtained using commercially offered sensors via a range of non-invasive procedures that follow conventional clinical apply. EAs will then be used to classify human information for a range of uses, as well as diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and while not genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is created for a computational approach which will be applied across human and animal studies of PD and lays the method for analysis of existing and new drug therapies during a truly objective approach.

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