  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 data set of patients clinical variables data
with Parkinsons Disease (PD) to study the disease progression. The data set
includes 22 properties of 42 people that all of our algorithms are applied to
this data set. The Decision Table with 0.9985 correlation coefficients has the
best accuracy and Decision Stump with 0.7919 correlation coefficients has the
lowest accuracy.
