Comparative Analysis Of The Early Detection Of Parkinson's Disease

Document Type : Primary Research paper


Assistant professor, Computer Application Department & Affiliated to Bharathidasan University, Bishop Heber College, Tiruchirappalli


In medical science, the data mining approach is now used to evaluate vast amounts of medical data. This study aims at examining Parkinson's diseases using the feature selection process. Parkinson's disease is a central nervous system degenerative condition that primarily affects the motor system because of dopamine loss, a chemical which transmits a message to the brain part for motion control. Parkinson's early identification is really challenging, so we Modified Whale Optimization (WOA) is used to select the significant feature from the dataset. These process can select the important and relevant data from large dataset, it help to classify the PD easily. In this study we mainly analysis the detection of Parkinson diseases by using different classifiers such as K-NN, DT, NB, GMM and K-means. We take this classifier to classify the diseases in idea of comparative study among this classifier model. In this different classifier performance parameters are measured by using different parametric calculations. By finally, conclude that the K-means classifier provide the better performance result than other classifier as accuracy of 95.64% respectively.