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

Kanghyeon Seo,Sunjae Won,Doyoung Jeon,Donkyu Kim,Jaewon Beom,Hyejung Park,Bokjin Chung,Byungmo Oh,Taewoo Kim,Jihoon Yang,Hamsa Priya Panchaseelan,Minho Chun

doi:10.3390/diagnostics11061096

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 accuracy, recall, precision, and F1-score. In particular, AutoGluon not only presented the highest predictive performance (almost in accuracy, recall, precision, and F1-score, and 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.

Highlights

Cerebrovascular accidents (CVA), i.e., strokes, could lead to walking impairments ranging from mild gait imbalance to complete loss of mobility for patients
Considering the time spent on the procedure of 5-CV (DT, LightGBM, and AutoGluon took 0.07, 0.09, and 12 min, respectively, whereas 15 min and 70 min were needed for TabNet and SuperTML, respectively), we found that the tree-based classification models are more efficient for learning from our dataset compared to the two deep learning (DL) models, though the performance of Decision Tree (DT) was 3.4% to 5% lower than that of the DL models
We evaluated the classification performance of machine learning (ML) and DL models for forecasting stroke patients’ walking assistance levels using a dataset gathered from different hospitals

Summary

Introduction

Cerebrovascular accidents (CVA), i.e., strokes, could lead to walking impairments ranging from mild gait imbalance to complete loss of mobility for patients. In the medical domain, numerous studies have been conducted, including cancer detection with image classification [7], a patient modeling system for clinical demonstration [8], an emergency screening system that differentiates acute cerebral ischemia and stroke mimics [9], a gait monitoring system that predicts stroke disease [10], etc. In the rehabilitation domain, walking assistance robot development [11], AI-based virtual reality rehabilitation [12], and forecasting mortality of stroke patients after complete rehabilitation with tree-based ML models [13] have been studied. There exist similar studies [14,15]

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Conclusion