首页|Reports Outline Machine Learning Research from College of Health Science (Classi fication of chronic ankle instability using machine learning technique based on ankle kinematics during heel rise in delivery workers)
Reports Outline Machine Learning Research from College of Health Science (Classi fication of chronic ankle instability using machine learning technique based on ankle kinematics during heel rise in delivery workers)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented.According to news reporting out of Wonju,South Korea,b y NewsRx editors,research stated,"Ankle injuries in delivery workers (DWs) are often caused by trips,and high recurrence rates of ankle sprains are related t o chronic ankle instability (CAI).Heel rise requires joint angles and moments s imilar to those of the terminal stance phase of walking that the foot supinates." The news correspondents obtained a quote from the research from College of Healt h Science:"Thus,our study aimed to develop,determine,and compare the predict ive performance of statistical machine learning models to classify DWs with and without CAI using ankle kinematics during heel rise.In total,203 DWs were scre ened for eligibility.Seven predictors were included in our study (age,work dur ation,body mass index,calcaneal stance position angle [CSPA ] in the initial and terminal positions during heel rise,cal caneal movement during heel rise [CM HR ],and plantar flexion angle during heel rise).Six machine learning algorithms,i ncluding logistic regression,decision tree,AdaBoost,Extreme Gradient boosting machines,random forest,and support vector machine,were trained.The random f orest model (area under the curve [AUC],0 .967 [excellent]; F1,0.889; accuracy,0.9 25) confirmed the best predictive performance in the test datasets among the six machine learning models.For Shapley Additive Explanations,old age,low CMHR,high CSPA in the initial position,high PFA,long work duration,low CSPA in the terminal position,and high body mass index were the most important predictors of CAI in the random forest model."
College of Health ScienceWonjuSouth KoreaAsiaCyborgsEmerging TechnologiesMachine Learning
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