Diagnosis of parkinson's disease using modified grey wolf optimization
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No specific test exists to diagnose Parkinson's disease. Your doctor trained in nervous system conditions (neurologist) will diagnose Parkinson's disease based on your medical history, a review of your signs and symptoms, and a neurological and physical examination. Your doctor may suggest a specific single-photon emission computerized tomography SPECT scan called a dopamine transporter (DAT) scan. Although this can help support the suspicion that you have Parkinson's disease, it is your symptoms and neurologic examination that ultimately determine the correct diagnosis. Most people do not require a DAT scan.
This paper presents the Modified Grey Wolf Optimization (MGWO) algorithm which helps with the identification of the symptoms of Parkinson’s disease at a premature stage. Parkinson disease is kind of a movement malady, which if not cured timely can prove to be fatal.
Thus it becomes significant to identify Parkinson’s disease at its premature phase so proper medications can provide longevity to patient by controlling the symptoms. In this work, a new model named Modified Grey Wolf Optimization (MGWO) has been proposed grounded on the traditional Grey Wolf Optimizer (GWO), which acts as a search strategy for feature selection. GWO is a meta-heuristic algorithm which is enthused by hunt down behavior of wolves. Random forest, k-nearest neighbor classifier and decision tree espy on selected features. The proposed model is evaluated using various types of datasets of voice, handwriting (spiral and meander) and speech.