Personalized prediction system for early prediction of freezing of gait in Parkinson's disease using explainable AI

Elbatanouny, H, Hussain, A, Al-Shabi, M, Khan, W ORCID: 0000-0002-7511-3873 and Mahmoud, S (2026) Personalized prediction system for early prediction of freezing of gait in Parkinson's disease using explainable AI. Royal Society Open Science, 13 (1). ISSN 2054-5703

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Abstract

Freezing of gait (FOG) is a common symptom of Parkinson's disease (PD), characterized by sudden and temporary episodes of immobility, often resulting in falls and reduced quality of life. Early and accurate prediction of FOG can greatly improve patient outcomes by allowing timely intervention and tailored treatment strategies. This article presents a personalized system for the early prediction of gait freezing in PD patients using explainable artificial intelligence (XAI). The proposed system follows a subject-dependent approach, training models specifically for individual patients to enhance prediction accuracy. It utilized multiple explainability techniques to ensure transparency in the decision-making process, achieving an average accuracy of 98.67 ± 0.75% using the random forest (RF) model and a latency of 75.0 ± 31.1 ms across six patients. Feature importance analysis, including SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) plots, revealed significant influences of features like the maximum value of the x-axis, the maximum of the accelerometer signal from the x-axis and the standard deviation of the gyroscope signal from the y-axis in classifying gait states.

Item Type:	Article
Uncontrolled Keywords:	freezing of gait; Parkinson's disease; machine learning; explainable AI; personalized Parkinson's disease; 4605 Data Management and Data Science; 46 Information and Computing Sciences; Neurodegenerative; Neurosciences; Parkinson's Disease; Precision Medicine; Brain Disorders; Aging; Bioengineering; Clinical Research; Machine Learning and Artificial Intelligence; Neurological; 3 Good Health and Well Being
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Computer Science and Mathematics
Publisher:	The Royal Society
Date of acceptance:	22 December 2025
Date of first compliant Open Access:	11 May 2026
Date Deposited:	11 May 2026 15:02
Last Modified:	11 May 2026 15:02
DOI or ID number:	10.1098/rsos.250818
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28552

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