A Comparative Study on Remote Tracking of Parkinsons Disease Progression Using Data Mining Methods

In recent years, applications of data mining methods are become more popular in many fields of medical diagnosis and evaluations. The data mining methods are appropriate tools for discovering and extracting of available knowledge in medical databases. In this study, we divided 11 data mining algorithms into five groups which are applied to a dataset of patient’s clinical variables data with Parkinson’s Disease (PD) to study the disease progression. The dataset includes 22 properties of 42 people that all of our algorithms are applied to this dataset. The Decision Table with 0.9985 correlation coefficients has the best accuracy and Decision Stump with 0.7919 correlation coefficients has the lowest accuracy.