• Vijay Venkatraman Naik
• Vasundar Bhargav M
• Savitha P

Parkinson's disease (PD) is a serious neurological ailment that impairs human motion, movement capabilities. Because it affects middle-aged and older individuals, who typically have uncoordinated cognitive and neuromuscular activities, it is one of the most frequent chronic neurological diseases. Speech difficulties are seen in and around 90% of those who have this illness. Information is produced in real-world applications utilizing a variety of machine learning approaches. The use of machine learning algorithms to identify diseases in their earliest stages helps older people live longer. With regard to the word "Parkinson's," speech characters are the key idea. In this study, the author employs a number of machine learning algorithms, includes KNN, Naive Bayes, Random Forest and XGBoost and how these algorithms are implemented in order to predict Parkinson’s based on the input taken from the individual and the input for algorithms i.e., the dataset. Prediction is crucial to treating patients when they are still in the early stages. Machine learning can be used to complete this procedure.

Parkinson’s disorder, Machine Learning, Speech disorders, KNN, Naïve Bayes, Random Forest, XGBoost

"Parkinson’s Disease Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.f439-f444, May-2023, Available :http://www.jetir.org/papers/JETIR2305565.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.10, Issue 5, page no. ppf439-f444, May-2023, Available at : http://www.jetir.org/papers/JETIR2305565.pdf