CHAPTER - 8

In the present decade of accelerated advances in Medical Sciences, most studies fail to lay focus on ageing diseases. These are diseases that display their symptoms at a much advanced stage and makes a complete recovery almost improbable. Parkinson’s disease (PD) is the second most commonly diagnosed neurodegenerative disorder of the brain. One could argue, that it is almost incurable and inflicts a lot of pain on the patients. All these make it quite clear that there is an oncoming need for efficient, dependable and expandable diagnosis of Parkinson’s disease. 

Managing PD in day-to-day life is very challenging for an individual. Therefore, a good screening procedure will be beneficial, especially in circumstances where a physician’s treatment is not necessary. Thus, for the diagnosis of PD, ML algorithms were evaluated. The main aim of this was to identify existing ML-based research to diagnose PD in terms of handwritten patterns, voice attributes, and to determine the most appropriate technique to diagnose the PD with an accuracy rate. This report also mentions the developments in neural networks and related learning systems, which provide valuable insights and guidelines for future progress. In future work, we can focus on different techniques to predict the Parkinson disease using different datasets. In this research, we using binary attribute [ 1 - diseased patients, 0 - non-diseased patients ] for patient’s classification. 

In the future we will use different types of attributes for the classification of patients and also identify the different stages of Parkinson's disease which really helps especially for the families who cannot afford the expensive MRI scans. In the future as this will be more advanced where we can use the position and the movements of the body to identify the disease with more accuracy. We observed that there is still a lot of work that has to be performed in the future.