Machine learning algorithms in spatiotemporal gait analysis can identify patients with Parkinson’s disease

Changes to spatiotemporal gait metrics in gait-altering conditions are characteristic of the pathology. This data can be interpreted by machine learning (ML) models which have recently emerged as an adjunct to clinical medicine. However, the literature is undecided regarding its utility in diagnosing pathological gait and is heterogeneous in its approach to applying ML techniques. This study aims to address these gaps in knowledge. This was a prospective observational study involving 32 patients with Parkinson’s disease and 88 ‘normative’ subjects. Spatiotemporal gait metrics were gathered from all subjects using the MetaMotionC inertial measurement unit and data obtained were used to train and evaluate the performance of 10 machine learning models. Principal component analysis and Genetic Algorithm were amongst the feature selection techniques used. Classification models included Logistic Regression, Support Vector Machine, Naïve – Bayes, Random Forest, and Artificial Neural Networks. ML algorithms can accurately distinguish pathological gait in Parkinson’s disease from that of normative controls. Two models which used the Random Forest classifier with Principal Component analysis and Genetic Algorithm feature selection techniques separately, were 100% accurate in its predictions and had an F 1 score of 1. A third model using principal component analysis and Artificial neural networks was equally as successful (100% accuracy, F 1 = 1). We conclude that ML algorithms can accurately distinguish pathological gait from normative controls in Parkinson’s Disease. Random Forest classifiers, with Genetic Algorithm feature selection are the preferred ML techniques for this purpose as they produce the highest performing model. Author summary The way humans walk, are emblematic of their overall health status. These walking patterns, otherwise, can be captured as gait metrics from small and portable wearable sensors. Data gathered from these sensors can be interpreted by machine learning algorithms which can then be used to accurately distinguish healthy and non-healthy patients based on their gait or walking pattern. The applications of this technology are many and varied. Firstly, it can be used to simply aid in diagnosis as explored in this paper. In future, researchers may use their understanding of normal and pathological gait, and their differences to quantify how severely one’s gait is affected in a disease state. This data can be used to track, and quantify, improvements or further deteriorations post treatment, whether these be medication-based or interventions like surgery. Retrospective analyses on data such as this can be used to judge the value of an intervention in reducing a patient’s disability, and advise health related expenditure. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NA ### Funding Statement No funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Approval was obtained from the South-Eastern Sydney Local Health District, New South Wales, Australia (HREC 17/184). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Able to be accessed in a Google Drive link. Deidentified Normative Database: https://docs.google.com/spreadsheets/d/1L2ua-LERcYig1LzS2DwjU-g0PVE1SKqfS69j8WcKIZ8/edit?usp=sharing Deidentified Parkinson's Database: https://docs.google.com/spreadsheets/d/1Sc6JL0UmtiEIJCmD1R2jbsuGXDIy24SxbmkdQKDJg-4/edit?usp=sharing