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Forecasting the Walking Assistance Rehabilitation Level of Stroke Patients Using Artificial Intelligence

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Abstract
Cerebrovascular accidents (CVA) cause a range of impairments in coordination, such as a spectrum of walking impairments ranging from mild gait imbalance to complete loss of mobility. Patients with CVA need personalized approaches tailored to their degree of walking impairment for effective rehabilitation. This paper aims to evaluate the validity of using various machine learning (ML) and deep learning (DL) classification models (support vector machine, Decision Tree, Perceptron, Light Gradient Boosting Machine, AutoGluon, SuperTML, and TabNet) for automated classification of walking assistant devices for CVA patients. We reviewed a total of 383 CVA patients' (1623 observations) prescription data for eight different walking assistant devices from five hospitals. Among the classification models, the advanced tree-based classification models (LightGBM and tree models in AutoGluon) achieved classification results of over 90% accuracy, recall, precision, and F1-score. In particular, AutoGluon not only presented the highest predictive performance (almost 92% in accuracy, recall, precision, and F1-score, and 86.8% in balanced accuracy) but also demonstrated that the classification performances of the tree-based models were higher than that of the other models on its leaderboard. Therefore, we believe that tree-based classification models have potential as practical diagnosis tools for medical rehabilitation.
Author(s)
김동규김태우박혜정범재원서강현양지훈오병모원선재전도영전민호정복진Hamsa Priya Panchaseelan
Issued Date
2021
Type
Article
Keyword
AccuracyAlgorithmsArtificial intelligenceautomated diagnosticsClassificationDatasetsDecision treesdeep learningdiagnostic reasoningGaitLibrariesmachine learningmedical decision makingPatientsRehabilitationStandard deviationStrokestroke rehabilitationSupport vector machineswalking assistance device
DOI
10.3390/diagnostics11061096
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9608
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_da6c9a0dca604cfcabf691f1c051a82e&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Forecasting%20the%20Walking%20Assistance%20Rehabilitation%20Level%20of%20Stroke%20Patients%20Using%20Artificial%20Intelligence&offset=0&pcAvailability=true
Publisher
DIAGNOSTICS
Location
벨기에
Language
영어
ISSN
2075-4418
Citation Volume
11
Citation Number
5
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Natural Science > ETC
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