Data mining and machine learning perspectives for occupational safety and health

Abstract

Introduction:

variety, volume and data generation speed allow for new and more complex analyses.

Objective:

to discuss and present data mining and machine learning techniques to aid occupational safety and health (OSH) researchers to choose the suitable technique when dealing with large volumes of data.

Methods:

literature review to discuss data mining and machine learning predictive applications for aiding diagnosis and risk prevention in OSH.

Results:

literature shows that data mining with machine learning algorithms for predictive purposes in OSH and public health present better performance when compared to traditional analysis. According to the research purpose, different techniques are recommended.

Discussion:

data mining has become a common alternative when dealing with large databases in public health, making it possible to analyze large volume of morbidity and mortality data. These techniques are not meant to replace the human factor, but rather to assist in decision-making processes, to work as a tool for the statistical analysis of OSH data and to build up knowledge to subsidize actions that may improve worker’s quality of life.

Keywords:
data mining; machine learning; occupational safety and health