State-of-the-Art Techniques for Detecting the Onset of Parkinson's Disease

Parkinson's disease which is a neurodegenerative disease tends to deteriorate muscle control steadily. Although Parkinson's disease primarily affects elderly people, but adults can also be affected by this disease. The symptoms are caused by the gradual nerve cell loss in the area of the brain that regulates movement. Patients' quality of life is greatly reduced because it shows motor, cognitive, and other sorts of symptoms. Parkinson's disease is usually diagnosed based on a patient's medical history and physical symptoms. However, it has a subjective nature and a poor prognosis. In the diagnosis of Parkinson's disease, artificial intelligence (AI) has shown promising results. Yet because of the small sample size, poor validation and lack of big data design, it introduces bias. This paper outlines the state-of-the-art methods that have been used to detect Parkinson's disease. The findings of the literature, as well as frequent methodological flaws that may skew results, are identified. The question is can AI alone be successful in clinical settings for Parkinson's disease identification? By incorporating AI algorithms with Internet of Things (IoT) treatment can be improved effectively while reducing related healthcare expenditures.