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  • 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 average accuracy of the proposed approach is 99 60, 97 8, and 94 5% for gait, handwriting, and voice parameters, respectively In contrast to other compared supervised classifiers, the modified kNN algorithm is more efficient in detecting Parkinson’s patients regardless of sample sizes
  • Parkinson’s Disease Detection by Using Machine Learning
    Additionally, multimodal methods that use fusion models to combine handwriting, motion, and voice characteristics have shown increased detection accuracy, up to 97 1 percent [3] This project uses both handdrawn pattern data and audio speech recordings to develop a deep learning solution that can distinguish between people with Parkinson's disease and those who are healthy Additionally, a variety of machine learning algorithms are assessed and contrasted, with a focus on the efficacy of KNN
  • Modified Federated Learning for Parkinsons Disease Prediction
    Parkinson's Disease (PD) is a common neurodegenerative condition characterized by tremors, stiffness, and bradykinesia Early PD detection and prediction are critical for optimizing treatment regimens and improving patient outcomes This research investigates the efficacy of machine learning algorithms in PD diagnosis and progression prediction
  • Enhancing Parkinson’s Disease Diagnosis Using Logistic Regression and KNN
    Parkinson’s disease is a neurogenerative condition that affects motor control The condition appears when dopamine-producing brain neurons, which are essential for controlling smooth muscle, begin to fail Early diagnosis of Parkinson’s disease motor symptoms allows a patient to receive appropriate care at the appropriate moment The most suitable machine learning method for Parkinson disease diagnosis is compared in this work between logistic regression and k-nearest neighbor (KNN
  • Advanced comparative analysis of machine learning algorithms for early . . .
    This study systematically assessed the performance of sev-eral supervised machine learning models for PD prediction, including LR, KNN, and SVM with linear and RBF kernels, AdaBoost, KDE, and ANN
  • Parkinson Disease Prediction using KNN Model followed by . . . - GitHub
    This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD) Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column)
  • Vol 24 Issue 05, MAY, 2024 Prediction of Parkinsons disease Using . . .
    istic Regression, to predict Parkinson’s disease based on user input and 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
  • 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 individuals with PD and healthy individuals based on voice signal characteristics
  • 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





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