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This research study proposes a classification system to identify and prevent occupational accidents in fiber storage warehouses at a pulp and paper facility. The present analysis is based on variables including pedestrian circulation, bobcat, trailer trucks, access, pedestrian circulation zones, and handrails. The proposed methodology defines and trains the system by using occupational accident event data collected at the facility. Three different predicting algorithms are used: J48 decision-making trees, Naive Bayes, and Bayesian nets. The results show that the J48 decision-making tree algorithm accurately identifies possible occupational accidents 90% of the time. It is concluded that identifying variables involved in occupational accidents allows generating a C4.5 (J48) decision-making tree that can be used as a support tool to prevent occupational accidents. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology based on the application of Support Vector Machine algorithms, Naïve Bayes and Genetic Algorithms in diagnostics of psychosocial evaluations for the identification and prediction of the psychosocial risk level of public-school teachers in Colombia. A comparative study of the model of machine learning for prediction was carried out: Support Vector Machines (SVM) and Naïve Bayes, in two stages, first with all the variables and second, reducing the dimensionality of the database applying genetic algorithms, The best forty variables with the best efficiency in prediction accuracy were selected. The database used consisted of 3000 epidemiological records, which corresponded to teachers from public schools in the metropolitan area of a Colombian city. The use of SVM easily detected variables of physiological type and the best prediction performance was obtained with accuracy of 96.3%. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology based on machine learning techniques in diagnostics of psychosocial assessments to identify the risk level in teachers of public schools in Colombia. A comparative study of three important models of machine learning for prediction was done: artificial neural networks, decision trees and naive bayes, reducing the dimensionality of the data. This was done by applying genetic algorithms, algorithm of the expected amount of information, the algorithm GainRatioAttributeEval, Pearson's relation coefficient and principal components analysis. A database was used with 5340 epidemiological records, corresponding to psychosocial evaluations of teachers from public schools in the metropolitan area of a Colombian city. The best predictive performance was obtained with the model of artificial neural networks with an accuracy 93%. [ABSTRACT FROM AUTHOR]