CHAPTER - 7

CONCLUSION

Over the past 30 years, our knowledge of PD has significantly increased, with the recognition of a prodromal phase, risk, and clinical markers, along with genetic variations in PD. A trial design symposium, summarizes and highlights the progress that has been made and which, as a result, has allowed the PD field to begin contemplating identification trials.

 As per the above elaborated review on the disease of Parkinson’s, it clearly construed that the detection of this disease needs so much effort. The detection process using machine learning technology helps the process of detection easy for humans in less cost, such that treatment and support can be provided to patients as soon as possible. 

Here, we presented included studies in a high-level summary, providing access to information including :

(a) machine learning methods that have been used in the diagnosis of PD and associated outcomes

(b) types of clinical, behavioral and biometric data that could be used for rendering more accurate diagnoses

(c) potential biomarkers for assisting clinical decision making, and 

(d) other highly relevant information, including databases that could be used to enlarge and enrich smaller datasets. 

In summary, realization of machine learning-assisted diagnosis of PD yields high potential for a more systematic clinical decision-making system, while adaptation of novel biomarkers may give rise to easier access to PD diagnosis at an earlier stage. Hence, the best efforts to be kept on finding the mechanism for the detection of the disease. The proposed project is to find Parkinson`s disease using voice data sets for testing by XGBoost algorithm and examine handwritten drawings by using random forest algorithm. The analysis of the voice and drawings data gives the accurate performance in detecting the PD. 

The accuracy of the proposed project for speech data is 100% and for handwritten spiral drawings  83.333%.