Authors:
Antonio Di Noia
1
;
Paolo Montanari
2
and
Antonello Rizzi
3
Affiliations:
1
University of Rome "La Sapienza", Italy
;
2
National Institute for Insurance against Accidents at Work (INAIL), Italy
;
3
University of Rome - "Sapienza", Italy
Keyword(s):
Occupational Diseases, Risk Prediction, Computational Intelligence, Cluster Analysis, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
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.