• Roshan D. Gupta
• Mr. Shivam A.Dubey
• Mr. Amrut R. Gaikwad
• Mr. Pawan M. Rana
• Prof. Shrikrishna D. Patil

One of the most destructive and moderate sensory system sicknesses that influence versatility is Parkinson's. It is one of the most dangerous neurological diseases, second only to Alzheimer, because it shortens life expectancy and affects posture, both of which have no known cure. Discourse issues impact around 90% of the people who have this ailment. In real-world applications, a variety of machine learning methods are used to generate information. The early discovery of ailments utilizing AI calculations assists more seasoned individuals with living lengthier. The characteristics are taken from the audio of people with Parkinson's disease who have a local binary pattern. While thinking about "Parkinson's," discourse attributes are the significant accentuation. The author of this study uses a variety of machine learning techniques, such as KNN, to predict Parkinson's disease using data from a dataset and user input. There are 195 people in the Parkinson's dataset, of which 48 had the disease and 147 were in good health. There were three records for every patient. The accuracy on the validation set was higher than 87 percent, which is comparable to other methods that are currently in use. A number of parameters, including MDVPs, Jitter, Shimmer, NHR, DFA, and RPDE, are included in the dataset. Based on these factors, the author predicts the algorithm with greater accuracy. When treating patients when they are still in the early stages, prediction is essential. The author has developed a website on which the results can be viewed. CSS and HTML have been used to create websites. It furnishes us with exact information, precise data, and results. AI can be utilized to finish this method. The result may be either 0 or 1 after the user interface has received the parameter value.

Parkinson's disease, nervous system illnesses, mobility, speech problems, machine learning, KNN algorithm, early detection, local binary pattern, dataset, accuracy, MDVPs, Jitter, Shimmer, NHR, DFA, RPDE, prediction, website, CSS, HTML, user interface.

"Parkinson’s Diseases Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.l474-l477, April-2023, Available :http://www.jetir.org/papers/JETIR2304B68.pdf

"Parkinson’s Diseases Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppl474-l477, April-2023, Available at : http://www.jetir.org/papers/JETIR2304B68.pdf