CAMPBELLFORD COMMUNITY RESOURCE CENTREBusiness Directories,Company Directories

Company Name: Corporate Name:	CAMPBELLFORD COMMUNITY RESOURCE CENTRE
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Company Address:	36 Front S,CAMPBELLFORD,ON,Canada
ZIP Code: Postal Code:	K0L
Telephone Number:	7056535161
Fax Number:	7056535203
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USA SIC Code(Standard Industrial Classification Code):	81900
USA SIC Description:	EMPLOYEE ASSISTANCE PROGRAMS
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Credit History: Credit Report:	Institution
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Parkinson Disease Prediction using KNN Model followed by . . . - GitHub
Parkinson Disease Detection using KNN and Making WebApp through Heroku This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD)
Detection of Parkinson disease using multiclass machine learning . . .
In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between
A modified kNN algorithm to detect Parkinson’s disease
The paper presents an efficient modification of traditional kNN for accurate diagnoses of Parkinson’s Disease on three different symptomatic data, which are disturbed posture, impaired voice, and cramped handwriting
Early detection of Parkinsons disease using machine learning
Through the findings of this paper, we aim to promote the use of ML in telemedicine, thereby providing a new lease of life to patients suffering from Parkinson's disease
PREDICTION OF PARKINSON DISEASE USING KNN ALGORITHM. - JETIR
Benba, Achraf, et al “Voiceprints Analysis Using MFCC and SVM for Detecting Patients with Parkinson's Disease ” 2015 International Conference on Electrical and Information Technologies (ICEIT), 2015
Predicting Parkinson’s Disease using Machine Learning
A paper (2002), Machine Learning Methods for Predicting Parkinson’s Disease Progression, evaluated the predictive ability of Support Vector Machine (SVM) and k-Nearest Neighbors (KNN) approaches to PD classification
Prediction of Parkinson’s Disease Using Machine Learning Methods
The detection of Parkinson’s disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk
Vol 24 Issue 05, MAY, 2024 Prediction of Parkinsons disease Using . . .
a relevant dataset The study aims to determine which algorithm provides the highest accuracy The results show that KNN achieves an accuracy of 80%, Logistic Regression 79%, and Naïve Bayes the highest at 81%, making it the p
Parkinson’s Disease Prediction - IJNRD
In this paper, the author uses various machine learning techniques, such as KNN, Naive Bayes, and Logistic Regression, and describes how these algorithms can be used to predict Parkinson’s disease based on user input and how they work
Early Prediction of Parkinsons Disease with Machine Learning: A KNN . . .
The implementation involves predicting Parkinson’s disease with an accuracy score of 98% using the KNN model and deploying it in a web app This innovative approach ensures wider accessibility and encourages patient self-management