Detecting Parkinson Disease in a Patient by Best Accuracy Using Machine Learning Approach
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Publication date: 2019-11-25
Eurasian J Anal Chem 2018;13(3):em2018160
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, uni-variate 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.