Detecting Parkinson Disease in a Patient by Best Accuracy Using Machine Learning Approach

Abstract

Parkinson’s disease is the most prevalent neurodegenerative disorder affecting
more than 10 million people worldwide. There is no single test which can be administered
for diagnosing Parkinson’s disease. Because of these difficulties, to investigate a machine
learning approach to accurately diagnose Parkinson’s, using a given dataset. To prevent
this problem in medical sectors have to predict the disease affected or not by finding
accuracy calculation using machine learning techniques. The aim is to explore machine
learning founded techniques for Parkinson disease by prediction outcomes in best
accuracy with finding classification report. The analysis of dataset by supervised machine
learning technique(SMLT) to capture several information’s like, variable identification, univariate analysis, bi-variate and multi-variate analysis, missing value treatments and
analyze the data validation, data cleaning/preparing and data visualization will be done on
the entire given dataset. To propose, a machine learning-based method to accurately
predict the disease by speech and tremor symptoms by prediction results in the form of
best accuracy from comparing supervise classification machine learning algorithms.
Additionally, to compare and discuss the performance of various machine learning
algorithms from the given transport traffic department dataset with evaluation
classification report, identify the result shows that the effectiveness of the proposed
machine learning algorithm technique can be compared with best accuracy with precision,
Recall and F1 Score