An intelligent system for monitoring and predicting Parkinson's disease: A review.

Parkinson's disease is a degenerative condition that greatly affects the population, especially the elderly. The main cause is the gradual death of melanocytes in the brain, which produces dopamine and transmit nerve signals during motor and cognitive activities. The disease has various symptoms, including motor and non-motor, which appear after the level of dopamine in the melanocytes reaches less than 40% of its normal levels. Early detection of the disease is crucial to prevent patients from reaching catastrophic stages that affect their quality of life. Artificial intelligence technologies, such as machine learning, deep learning, and the Internet of Things (IOT), provide important benefits in predicting, detecting, and tracking disease progression. This review examines previous literature on AI techniques and their use in finding successful solutions to reduce disease progression. Researchers focused on prediction and early detection using medical technologies such as brain MRI scans. In contrast, others focused on clinical symptoms such as acoustic measurements, gait analysis, and electroencephalography, in addition to exploiting IOT technologies to track disease progression. All studies used approved criteria to test the efficiency of the models. proposed classifications, such as classification accuracy, sensitivity, and specificity, to determine their effectiveness in controlling the disease. The review findings highlight the status of intelligent systems and models proposed in the literature and identify their advantages, disadvantages, difficulties, and potential paths forward. These cutting-edge monitoring and prediction systems have the potential to transform the treatment of Parkinson's disease and provide invaluable assistance to patients and medical professionals, if technological advances continue. [ABSTRACT FROM AUTHOR]