Détail du document
Identifiant
oai:arXiv.org:2306.04748
Sujet
Computer Science - Machine Learnin...Auteur
Ram, AshwinCatégorie
Computer Science
Année
2023
Date de référencement
19/06/2024
Mots clés
parkinson disease pd machine learning researchRésumé
This paper represents a groundbreaking advancement in Parkinson disease (PD) research by employing a novel machine learning framework to categorize PD into distinct subtypes and predict its progression.
Utilizing a comprehensive dataset encompassing both clinical and neurological parameters, the research applies advanced supervised and unsupervised learning techniques.
This innovative approach enables the identification of subtle, yet critical, patterns in PD manifestation, which traditional methodologies often miss.
Significantly, this research offers a path toward personalized treatment strategies, marking a major stride in the precision medicine domain and showcasing the transformative potential of integrating machine learning into medical research.
;Comment: 15 Pages.
Machine Learning; Signal Processing; Parkinson's Disease
Ram, Ashwin, 2023, Analysis, Identification and Prediction of Parkinson Disease Sub-Types and Progression through Machine Learning
High-Frequency Repetitive Magnetic Stimulation at the Sacrum Alleviates Chronic Constipation in Parkinson’s Patients
magnetic stimulation parkinson’s significant patients scale sacrum pd hf-rms chronic constipation scores
