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Supervised machine learning techniques and genetic optimization for occupational diseases risk prediction

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Abstract

Workers healthcare gained a lot of attention recently as many countries are increasingly concerning about welfare. This paper faces the problem of predicting occupational disease risks by means of computational intelligence and pattern recognition techniques. Specifically, three different machine learning approaches are compared: the first one is based on the k-means algorithm, in charge to determine a set of meaningful labelled clusters as the final model. The latter two are based on fully supervised techniques, namely Support Vector Machines and K-Nearest Neighbours. Real data regarding both the worker and the workplace by mixing numerical and categorical attributes have been used for testing. The three approaches are automatically tuned by means of genetic algorithms in order to simultaneously find the optimal hyperparameters for the classification systems and the optimal ad-hoc dissimilarity measure weights in order to maximize the classification performances. Computational results show that the three approaches are rather comparable in terms of performances, but a clustering-based approach allows a deeper knowledge discovery phase, helpful for further risk assessment and forecasting.

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Notes

  1. Any pre-screening system able to minimize false positives and/or false negatives might have a major impact on (public) costs.

  2. An Italian version of the classification system ISCO (International Standard Classification of Occupations).

  3. An Italian version of the classification system NACE (Nomenclature des Activités Économiques dans la Communauté Européenne).

  4. Recall that the clustering approach also works in a One-Against-All fashion, thus the most popular cluster amongst the ones labelled as ‘sick’ (i.e. having carpal tunnel syndrome) and the most popular cluster amongst the ones labelled as ‘healthy’ (i.e. not having carpal tunnel syndrome) have been selected.

  5. Due to the classification speed, any constraints on prediction running times can be seen as automatically satisfied anyways.

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Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184, Rome, Italy

    Antonio Di Noia, Alessio Martino & Antonello Rizzi

  2. Research Department, National Institute for Insurance against Accidents at Work (INAIL), Rome, Italy

    Paolo Montanari

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  1. Antonio Di Noia
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  2. Alessio Martino
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  4. Antonello Rizzi
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Correspondence to Alessio Martino.

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Di Noia, A., Martino, A., Montanari, P. et al. Supervised machine learning techniques and genetic optimization for occupational diseases risk prediction. Soft Comput 24, 4393–4406 (2020). https://doi.org/10.1007/s00500-019-04200-2

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