Machine learning models for minimizing aggravation in work-related musculoskeletal disorders among slaughterhouse workers.
Background: Work-related musculoskeletal disorders (WMSDs) are common in Brazilian slaughterhouses. The repetitive and strenuous nature of meat processing, especially in slaughterhouses, makes employees highly susceptible to developing WMSDs. Prolonged standing, repetitive motions, and forceful acti...
| Publicado en: | Work Vol. 81; no. 4; pp. 3170 - 3184 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186840245&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186840245 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: Aug2025 vid: 81 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 186840245 184773550 186840245 186840245 10.1177/10519815251329261 186840245 ppf: 3170 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning models for minimizing aggravation in work-related musculoskeletal disorders among slaughterhouse workers. aug: au: Marzoque, Hercules José Batista, Marcelo Linon Nääs, Irenilza de Alencar de Alencar, Maria do Carmo Baracho affil: University Paulista, Department Graduate Program in Production Engineering, São Paulo, Brazil sug: subj: Machine Learning Prediction Models Occupational-Related Injuries Prevention and Control Occupational-Related Injuries Risk Factors Musculoskeletal Diseases Risk Factors Risk Assessment Food Industry Brazil Data Mining Artificial Intelligence Ergonomics Predictive Value of Tests Human Brazil Male Female Adult Middle Age Food Handling Occupational Diseases Descriptive Statistics Data Analysis Software kappa Statistic Blood Pressure Diabetes Mellitus Productivity Factor Analysis Random Forest Occupational Health Evaluation Work Environment Job Characteristics Conceptual Framework Data Analysis Sick Leave Age Factors Functional Status Time Factors Sex Factors Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Background: Work-related musculoskeletal disorders (WMSDs) are common in Brazilian slaughterhouses. The repetitive and strenuous nature of meat processing, especially in slaughterhouses, makes employees highly susceptible to developing WMSDs. Prolonged standing, repetitive motions, and forceful actions such as lifting and cutting are common contributing factors. Objective: This study aimed to develop models to predict the risk of aggravating WMSDs in slaughterhouse workers using the data mining concept. Methods: Data were retrieved from an open-source governmental database, and descriptive statistics were used to evaluate them. The data set involved organizational aspects, and demographic, physical, and health issues were attributes. A descriptive analysis was applied, and the data mining method was used to process data with the Random Forest algorithm to classify the aggravation of WMSDs'. Results: Three tree-ensemble predictive models were found (accuracy = 95.3%, κappa = 0.93) and described using the "If-Then" rules. The first tree had as the root attribute the change of function due to a health condition (high blood pressure or diabetes), followed by medical leave, working time, change of working place, and age, and the second had the worker's age as the root attribute, followed by working time, sex, and age. The third tree's root attribute was musculoskeletal pain symptoms, followed by working hours, age, and working time. Workers who do not change their roles and are on medical leave for over 1642.5 days present a high risk of worsening symptoms. Working time over 1980 days leads to a high risk of aggravating WMSDs. Females older than 24.5 years and staying more than 1620 days in the same function also presented a high risk of aggravating the WMSDs. Conclusions: The machine learning models might help prevent WMSD risk aggravation by sorting the available data set and identifying patterns and relationships. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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