Occupational Diseases Risk Prediction by Cluster Analysis and Genetic Optimization
2014
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 systemsâ development.
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