Diagnosing Parkinson’s Disease: its evolution to future

The proposed work entails a comprehensive analysis of the evolution of Parkinson’s Disease, associated symptoms, treatments, and its progression along with algorithmic approaches for diagnosis. Most of the work regarding this ailment focuses on diagnosing Parkinson-induced voice tremors, and the least is found in estimating the impact of medicines on its progression. Recent trends reveal the wide usage of feature selection and evolutionary algorithms to extract the subset of best-performing features that provides high accuracy rates by rejecting other similar parameters. Several research gaps have also been identified in this work, which can be explored in the future such as (i) certain evolutionary algorithms are never been used in Parkinson’s diagnosis, (ii) the impact of medicines on the progression of this disorder, (iii) efficient diagnosis of Parkinsonian disorder with a combination of multiple kinds of data, and other early symptoms like bradykinesia.