Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU‐Based Gait Analysis

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 threelevel
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.