Artificial Intelligence towards Parkinson’s disease Diagnosis: A systematic Review of Contemporary Literature

Parkinson's disease (PD) is a neurodegenerative illness with no known cure that causes significant disability and reduces quality of life. This space of the brain develops a neurotransmitter known as dopamine, which is generated by nerve cells. Dopamine has the crucial role of a messenger between brain and certain parts of the nervous system that are having crucial role in body movements. As dopamine is distributed more widely, neurons in the parts have difficulties speaking, writing, walking, and performing other simple activities. Approximately 90% of persons with Parkinson's disease experience one or more of tremors, gait imbalance and speech problems. Around the age 70 is of onset, and the occurrence increases dramatically as one gets older. However, only a tiny percentage of patients with PD get the condition even before age of 50. PD affects more than ten million people globally. There is presently no treatment is available to cure the Parkinson's disease, but research is ongoing, and medications or surgery can help with motor symptoms in some cases. PD is one of the most disturbing diseases. As a result, early detection may assist to prevent or mitigate the symptoms. Machine learning classification methods are employed to conclude whether or not someone has PD. This manuscript contributed a systematic review of contemporary literature about feature engineering strategies and machine learning models developed towards Parkinson’s disease prediction.