Parkinson‘s Disorder: Taking a Step towards Homogenizing Machine Learning and Medical Science

Machine learning is a scientific endeavor that has grown out of the need for analyzing and processing large scores of data. It aims towards inculcating the ability to learn within computer systems, thereby inciting a curiosity amongst science enthusiasts to explore the newly emerging field of technology. With machine learning being extensively used in everyday tasks, researchers are finding new ways of incorporating the technology in medical sciences, especially in neurodegenerative diseases like Parkinson’s disease. Parkinson’s is a major neurodegenerative disorder which is caused due to the dopamine deficit that occurs in the striatum part of the brain. It is the most sought-after disease in the field of neurological science due to its impact on a larger stratum of the world population. Therefore, the emergence of machine learning approaches to resolving the problems in the detection and treatment of this disease is a breakthrough in the field of clinical care. This review aims to analyze the less explored areas of treatment and diagnosis of PD. It addresses and evaluates the currently used computer-based modeling techniques and algorithms for the diagnosis of PD and emphasizes on specific aspects of treatment using machine learning. Hence, this review aims to unearth the gap between the recent works and the work that further needs to be done in the ambit of Parkinson’s disease.