###### Abstract  
Gait analysis is a valuable tool for aiding in the diagnosis of neurological diseases, providing objective measurements of human gait kinematics and kinetics. These data enable the quantitative estimation of movement abnormalities, which helps to diagnose disorders and assess their severity. In this regard, machine learning techniques and explainability methods offer an opportunity to enhance anomaly detection in gait measurements and support a more objective assessment of neurodegenerative disease, providing insights into the most relevant gait parameters used for disease identification. This study employs several classifiers and explainability methods to analyze gait data from a public dataset composed of patients affected by degenerative neurological diseases and healthy controls. The work investigates the relevance of spatial, temporal, and kinematic gait parameters in distinguishing such diseases. The findings are consistent among the classifiers employed and in agreement with known clinical findings about the major gait impairments for each disease. This work promotes the use of data-driven assessments in clinical settings, helping reduce subjectivity in gait evaluation and enabling broader deployment in healthcare environments.  
gait analysis; data mining; machine learning; explainable AI Article  
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Academic Editors: [PERSON], [PERSON] and [PERSON]  
11 July 202  
Moreover, these approaches can be supported by explainable AI (XAI) methods that make the classification process of the models understandable by humans. This aspect can be relevant in fields such as healthcare, since it can shed light on non-trivial relations between different measures, providing more insights for diagnosing or assessing a disease.  
Public datasets of gait analysis are fundamental to advancing research and developing innovative methodologies for analyzing such data. For instance, they can enable reproducibility and serve as a benchmark for comparing different analysis approaches. Indeed, several datasets are available for studying gait dysfunction in neurodegenerative diseases [2]. Some of them record floor sensor data, which measure ground reaction forces and provide insights into the center of pressure of the body [8; 9], whereas others record spatial, temporal, and kinematic features through motion analysis systems [10; 11]. In particular, the dataset [10] has been made available and employed in [12] to identify gait patterns in patients affected by neurodegenerative diseases through cluster analysis. The work underlined the importance of a subset of parameters able to distinguish healthy subjects from people affected by one of the neurological diseases considered (i.e., cerebellar ataxia, Parkinson's disease, and hereditary spastic paraplegia). Also, it gave insights into the parameters that better distinguish a neurological disease from the others.  
Similar considerations can be made using XAI techniques. For instance, in [13], these techniques were employed to evaluate the importance of several gait parameters, many of them being extracted from the gait profiles, in the identification of foot conditions. Other interesting applications of XAI methods in gait analysis can be found in [14], where the authors used wearable sensors to identify patients with osteopenia and sarcopenia.  
This work aims to identify the most relevant gait parameters for detecting neurodegenerative diseases by using a public dataset [10], which employed an established optoelectronic motion analysis system.  
To increase the robustness of the results and minimize model-specific biases, this work analyzed the spatial, temporal, and kinematic data obtained by the system using diverse supervised classifiers, such as Random Forest, gradient-boosted trees (i.e., XGBoost), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), and explainability techniques such as permutation importance [15] and SHapley Additive exPlanations (SHAP) [16] were applied to interpret the output of the classification.