Using machine learning techniques to classify factors that influence the occurrence of occupational dermatitis

Abstract

Introduction:

to predict work related diseases is a challenge for organizations and the governmental authorities. By means of machine learning (ML) techniques it is possible to identify factors that determine the occurrence of an occupational disease, aiming at taking more effective actions to protect workers.

Objective:

to predict, by comparing ML techniques, the factors which highly influence the occurrence of occupational dermatitis.

Methods:

we developed a code in R language and a descriptive analysis of the data and identified the influence factors according to the ML technique that presented the best performance. The database was made available by the Occupational Dermatology Service of Oswaldo Cruz Foundation and assembles information of the workers who experienced cutaneous alterations suggestive of occupational dermatitis between 2000-2014.

Results:

the techniques which presented the best performance were: neural network, random forest, support vector machine, and naive Bayes. Sex, schooling, and profession were the most adequate variables for the occupational dermatitis prediction models.

Conclusion:

ML techniques allowed to predict the factors that influence the workers’ safety and health, as well as the parameters that subsidize the procedures implementation, and the most effective policies to prevent occupational dermatitis.

Keywords:
occupational diseases; dermatitis, occupational; forecasting; machine learning; occupational health