Machine Learning Algorithms for Parkinson’s Disease Detection
AUTHORS
Ch Sekhar,Dept of Comp Sci. and Engineering, Vignan’s IIT(A), India
M S Rao,Dept of Comp Sci. and Engineering, Vignan’s IIT(A), India
D Bhattacharyya,Dept of Comp Sci. and Engineering, Vignan’s IIT(A), India
ABSTRACT
Machine learning now days plays a crucial role in real-time problem analysis and providing solutions with its popular algorithms. Nowadays, in the health care sector, machine learning algorithms are involved in detecting the health issues of patients. This paper elaborated detailed information about how the ML algorithms are detecting Parkinson’s disease. Parkinson’s sickness is caused by the interruption of the brain cells that generate an essence to permit synapses to speak with one another, called dopamine. The cells with the purpose of produce dopamine in the cerebrum are answerable for the control, adjustment and familiarity of developments. At the point when 60-80%of, these cells are missing, at that point adequate dopamine isn’t delivered, and Parkinson’s engine indications show up Here we used random forest and XGBoost algorithms to detect the disease XGBoost giving the best performance than the Random forest.
KEYWORDS
Parkinson’s disease, Random forest, XGBoost, Machine learning, Neural networks
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