Different Classification Approaches for Early Detection of Parkinson’s Disease

Abstract

Parkinson’s disease is perhaps the most well-known neurodegenerative disorder that mainly occurs due to the loss of dopamine-producing neurons and consists of motor/non-motor symptoms. The progression of the symptoms is often varying from one person to another to the diversity of the disease. The condition causes a huge burden both on those affected, as well as their families. Accurate diagnosis is critical and challenging but still, no specific diagnostic process is available. The computer-aided diagnosis techniques of signalling and imaging processing are very helpful in the prediction and classification of PD. This review gives a brief description of different methods of classification for early detection and also highlights the most profitable research directions by focusing on continuous monitoring patterns of daily activities, interactions, and routine that may provide the data on status changes, clinical management, and controlling self-correction