Student ID- 10101002 Supervisor

Parkinson Disease (PD) occurs due to the loss of dopamine in the brains thalamic region that results in involuntary or oscillatory movement in the body. Normally Doctors diagnosis the PD disease clinically with their expertise and experience. But most of the time wrong diagnosis and treatment are reported. For this, patients need to take number of tests for diagnosis, but most of the time, these all tests still not sufficient to diagnosis Parkinson Disease effectively. Firstly, this paper is proposed to apply some data mining technique to select the best attributes to increase the classification performance (according to the voice measurement datasets). In second step, Ada-Boost algorithm is applied to classify the Parkinson disease on the basis of Voice measurements data of PD patients. Then, Support vector Machine with Sequential Minimal Optimization classifier, is used to make the comparison with the result of Ada-Boost classifier to find out the best classifier. In addition, six other best classifiers ex: Naïve bayes, J48 Tree, LogitBoost, ADTree, BFTree, and Decision Stump Tree are used to make comparison with Parkinson dataset and to select the best classifier