• Ayesha Ekram

Parkinson disease is solitary of the feared diseases that affect elderly populations who indicate very slow response to treatment at cutting edge phases of the disease. Deep Belief Network can be deployed to obtain suitable features from the brain morphometry data and calculate its performance in distinguishing between the health controls & patients diseased amid Parkinson Disease. Different advances and calculations have been connected in the territory of Parkinson Disease, including information mining calculation like K-NN, PCA, Random Forest, Deep Belief Network, and Neural Network. It additionally reached a conclusion that K-NN has better exactness results on datasets or properties are effectively arranged. In this research paper, we have analyzed the Parkinson disease data using classification and dimensionality reduction method and found around 92 percentage successful results.

PCA ,KNN, Parkinson Disease, KNN-PCA algorithm

"An Approach of Classification and Dimensionality-reduction to Predict Parkinson Disease", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.b193-b196, May-2022, Available :http://www.jetir.org/papers/JETIR2205127.pdf

"An Approach of Classification and Dimensionality-reduction to Predict Parkinson Disease", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppb193-b196, May-2022, Available at : http://www.jetir.org/papers/JETIR2205127.pdf