首页|New Study Findings from Hamadan University of Medical Sciences Illuminate Resear ch in Machine Learning (Modeling the Impact of Ergonomic Interventions and Occup ational Factors on Work-Related Musculoskeletal Disorders in the Neck of Office ...)
New Study Findings from Hamadan University of Medical Sciences Illuminate Resear ch in Machine Learning (Modeling the Impact of Ergonomic Interventions and Occup ational Factors on Work-Related Musculoskeletal Disorders in the Neck of Office ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting from Hamadan, Iran , by NewsRx journalists, research stated, "Modeling with methods based on machin e learning (ML) and artificial intelligence can help understand the complex rela tionships between ergonomic risk factors and employee health. The aim of this st udy was to use ML methods to estimate the effect of individual factors, ergonomi c interventions, quality of work life (QWL), and productivity on work-related mu sculoskeletal disorders (WMSDs) in the neck area of office workers." Our news journalists obtained a quote from the research from Hamadan University of Medical Sciences: "A quasi-randomized control trial. To measure the impact of interventions, modeling with the ML method was performed on the data of a quasi -randomized control trial. The data included the information of 311 office worke rs (aged 32.04±5.34). Method neighborhood component analysis (NCA) was used to m easure the effect of factors affecting WMSDs, and then support vector machines ( SVMs) and decision tree algorithms were utilized to classify the decrease or inc rease of disorders. Three classified models were designed according to the follo w-up times of the field study, with accuracies of 86.5%, 80.3% , and 69 %, respectively. These models could estimate most influence r factors with acceptable sensitivity. The main factors included age, body mass index, interventions, QWL, some subscales, and several psychological factors. Mo dels predicted that relative absenteeism and presenteeism were not related to th e outputs."
Hamadan University of Medical SciencesHamadanIranAsiaCyborgsEmerging TechnologiesMachine LearningRisk and Prevention
NETL
NSTL
