A hybrid algorithm for the assessment of the influence of risk factors in the development of upper limb musculoskeletal disorders

Abstract

A hybrid model based on genetic algorithms, classification trees and multivariate adaptive regression splines is applied to identify the risk factors that have the strongest influence on the development of an upper limb musculoskeletal disorder using the data of the Spanish Seventh National Survey on Working Conditions. The study is performed among a sample of workers from the extractive and manufacturing industry sector, where upper limb have been the most frequently reported disorders during 2016. The considered variables are connected to employment conditions, physical conditions at workplace, safety conditions, workstation design and ergonomics, psychosocial and organizational factors, Health and Safety management and health damages. These variables are either continuous, Liker scale or binary. The chosen output variable is built taking into consideration the presence or absence of three conditions: the existence of upper limb pain, the perception of a work-related nature and the requirement of medical care in relation with it. The results show that WMSD have a multifactorial origin and the categories that include the most relevant variables are: ergonomics and psychosocial factors, workplace conditions and workers’ individual characteristics.