Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis.
Sensors (Basel)
; 22(10)2022 May 12.
Article
en En
| MEDLINE
| ID: mdl-35632109
ABSTRACT
The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson's disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors. Accuracy, precision, recall, and F1 score were calculated. SVM, DT, and RF showed the best classification performances, with prediction accuracy higher than 80% on the test set. The conceptual model of approaching ML that we proposed could reduce the risk of overrepresenting multicollinear gait features in the model, reducing the risk of overfitting in the test performances while fostering the explainability of the results.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Enfermedad de Parkinson
/
Análisis de la Marcha
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Año:
2022
Tipo del documento:
Article
País de afiliación:
Italia