Application of Data mining in Analysis and detection of Parkinson’s Disease

Parkinson's disease (PD) is a neurodegenerative disorder which often affects patients' movements. Some of the most common symptoms of Parkinson’s disease are tremors, rigidity, akinesia, walking disability, and postural instability. The primary motor symptoms are collectively called “parkinsonism”. This paper provides a brief description of the existing techniques used in detecting Parkinson’s Disease with the help of various data mining algorithms such as Multiple Instance Learning (MIL), K-means clustering, Decision Tree Classification, Moving Average Algorithm etc., their accuracies and drawbacks and also gives an outline of the proposed system. Since all of the existing models consider a single symptom for detecting Parkinson’s, the proposed approach aims at building an analytical model with two different symptoms i.e. speech and finger tapping keystroke, so as to increase the accuracy and find the corelation between these symptoms.