Occupational diseases risk prediction by genetic optimization. Towards a non-exclusive classification approach

This paper deals with the health risk prediction problem in workplaces through computational intelligence techniques. The available dataset has been collected from the Italian Local Health Authority (ASL) as part of the Surveillance National System. The main aim of this work is the design of a software application that can be used by occupational physicians in monitoring workers, performing a risk assessment of contracting some particular occupational diseases. The proposed algorithms, based on clustering techniques, includes a genetic optimization in order to automatically determine the weights of the adopted distance measure between patterns and the number of clusters for the final classifier’s synthesis. In particular, we propose a novel approach, consisting in defining the overall classifier as an ensemble of class-specific ones, each trained to recognize patterns of risk conditions characterizing a single pathology. First results are encouraging and suggest interesting research tasks for further system development.