Prediction of Parkinson Disease by Best Accuracy using Supervised Classification 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 investigate machine learning based techniques for Parkinson disease by prediction results 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, bivariate and multivariate analysis, missing value treatments and analyze the data validation, data cleaning/preparing and data visualization will be done on the entire given dataset