Journal
SENSORS
Volume 22, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/s22103700
Keywords
machine learning; artificial intelligence; gait analysis; Parkinson's disease; harmonic ratio; K-nearest neighbors; support vector machine; random forest; artificial neural network; decision tree
Funding
- INAIL
- European Union [871237]
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The aim of this study was to determine the most accurate supervised machine learning algorithm for classifying people with Parkinson's disease from healthy subjects based on gait features. The study found that support vector machine, decision trees, and random forest showed the best classification performances.
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.
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