Parkinson Disease Classification using

Knowledge discovery in databases has established its success rate in various prominent fields such as e-business, marketing, retail and medical. Medical data mining has great potency for exploring the out of sight patterns in the respective medical data sets. This paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use today for the classification of Parkinson Disease. Parkinson Disease is a chronic malady of the central nervous system where the key indications can be captivated from the Mentation, Activities of Daily Life (ADL), Motor Examination and Complications of Therapy. The speech symptom which is an ADL is a common ground for the progress of the disease. The dataset for the disease is acquired from UCI, an online repository of large data sets. A comparative study on different classification methods is carried out to this dataset by applying the feature relevance analysis and the Accuracy Analysis to come up with the best classification rule. Also the intention is to sieve the data such that the healthy and people with Parkinson will be correctly classified. General Terms Data Mining, Healthcare Data, Parkinson Disease.