Occupational diseases risk prediction by cluster analysis and genetic optimization

This paper faces the health risk prediction problem in workplaces through computational intelligence techniques applied to a set of data collected from the Italian national system of epidemiological surveillance. The goal is to create a tool that can be used by occupational physicians in monitoring visits, as it performs a risk assessment for workers of contracting some particular occupational diseases. The proposed algorithm, based on a clustering technique is applied to a database containing data on occupational diseases collected by the Local Health Authority (ASL) as part of the Surveillance National System. A genetic algorithm is in charge to optimize the classification model. First results are encouraging and suggest interesting research tasks for further systems' development.