• Shraddha V. Bante
• Shweta M. Barhate
• Satish J. Sharma

Parkinson's disease is a neurodegenerative disorder that affects millions of people around the world. Parkinson’s disease can affect a person’s voice, causing them to speak softly or have difficulty in forming sound clearly. Speech or voice data is assumed to be 90% helpful to diagnose the result. The proposed system used a data set parkinson.csv. A speech data set includes the number of voice features such as jitter, shimmer, pitch, and frequency. Different data preprocessing methods, such as data standardization technique to improve the quality of data. In the present work relevant features were then extracted using Mel Frequency Cepstral Coefficient (MFCC) algorithm. Classification is performed using an Support Vector Machine (SVM) algorithm to differentiate between healthy and people with Parkinson’s disease. The outcome of the proposed system is early detection of Parkinson's disease, which may help to better diagnose the disease.

Parkinson’s Disease, Speech Dataset, Machine Learning, MFCC, Support Vector Machine

"PARKINSON'S DISEASE DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.21-23, June-2022, Available :http://www.jetir.org/papers/JETIRFM06005.pdf

"PARKINSON'S DISEASE DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. pp21-23, June-2022, Available at : http://www.jetir.org/papers/JETIRFM06005.pdf