Automatic selection model to identify neurodegenerative diseases.
Digit Health
; 10: 20552076241284376, 2024.
Article
em En
| MEDLINE
| ID: mdl-39372807
ABSTRACT
Objective:
This study evaluates machine learning algorithms' effectiveness in classifying Parkinson's disease and Huntington's disease based on biomarker data obtained non-invasively from patients and healthy controls.Methods:
Datasets containing biomarker data (x, y, and z values of accelerometers) from sensors were collected from Parkinson's disease, Huntington's disease patients, and healthy controls. An automatic selection model method was implemented for disease classification, using a unique Mexican database of human gait biomarkers, which we consider the only one of its kind. Random forest, random subspace method, and K-star algorithms were employed, with parameters optimized through an automated model selection.Results:
The study achieved a 0.893 precision rate for Parkinson's disease and Huntington's disease using the random subspace method. The findings underscore the potential of machine learning techniques in medical diagnosis, particularly in neurological disorders.Conclusion:
The automatic selection model method demonstrated efficacy in classifying Parkinson's disease and Huntington's disease based on non-invasive biomarker data. This research contributes to advancing non-invasive diagnostic approaches in neurological disorders, highlighting the significance of machine learning in healthcare.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Digit Health
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
México
País de publicação:
Estados Unidos