Occupational Diseases Risk Prediction by Cluster Analysis and Genetic Optimization

Antonio Di Noia, Paolo Montanari, Antonello Rizzi

2014

Abstract

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.

Paper Citation

in Harvard Style

Di Noia A., Montanari P. and Rizzi A. (2014). Occupational Diseases Risk Prediction by Cluster Analysis and Genetic Optimization . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 68-75. DOI: 10.5220/0005077800680075

in Bibtex Style

@conference{ecta14,
author={Antonio Di Noia and Paolo Montanari and Antonello Rizzi},
title={Occupational Diseases Risk Prediction by Cluster Analysis and Genetic Optimization},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},
year={2014},
pages={68-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005077800680075},
isbn={978-989-758-052-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - Occupational Diseases Risk Prediction by Cluster Analysis and Genetic Optimization
SN - 978-989-758-052-9
AU - Di Noia A.
AU - Montanari P.
AU - Rizzi A.
PY - 2014
SP - 68
EP - 75
DO - 10.5220/0005077800680075