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Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers
BACKGROUND: Psychosocial risks, also present in educational processes, are stress factors particularly critical in state-schools, affecting the efficacy, stress, and job satisfaction of the teachers. This study proposes an intelligent algorithm to improve the prediction of psychosocial risk, as a to...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8176537/ https://www.ncbi.nlm.nih.gov/pubmed/34141875 http://dx.doi.org/10.7717/peerj-cs.511 |
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author | Mosquera Navarro, Rodolfo Castrillón, Omar Danilo Parra Osorio, Liliana Oliveira, Tiago Novais, Paulo Valencia, José Fernando |
author_facet | Mosquera Navarro, Rodolfo Castrillón, Omar Danilo Parra Osorio, Liliana Oliveira, Tiago Novais, Paulo Valencia, José Fernando |
author_sort | Mosquera Navarro, Rodolfo |
collection | PubMed |
description | BACKGROUND: Psychosocial risks, also present in educational processes, are stress factors particularly critical in state-schools, affecting the efficacy, stress, and job satisfaction of the teachers. This study proposes an intelligent algorithm to improve the prediction of psychosocial risk, as a tool for the generation of health and risk prevention assistance programs. METHODS: The proposed approach, Physical Surface Tension-Neural Net (PST-NN), applied the theory of superficial tension in liquids to an artificial neural network (ANN), in order to model four risk levels (low, medium, high and very high psychosocial risk). The model was trained and tested using the results of tests for measurement of the psychosocial risk levels of 5,443 teachers. Psychosocial, and also physiological and musculoskeletal symptoms, factors were included as inputs of the model. The classification efficiency of the PST-NN approach was evaluated by using the sensitivity, specificity, accuracy and ROC curve metrics, and compared against other techniques as the Decision Tree model, Naïve Bayes, ANN, Support Vector Machines, Robust Linear Regression and the Logistic Regression Model. RESULTS: The modification of the ANN model, by the adaptation of a layer that includes concepts related to the theory of physical surface tension, improved the separation of the subjects according to the risk level group, as a function of the mass and perimeter outputs. Indeed, the PST-NN model showed better performance to classify psychosocial risk level on state-school teachers than the linear, probabilistic and logistic models included in this study, obtaining an average accuracy value of 97.31%. CONCLUSIONS: The introduction of physical models, such as the physical surface tension, can improve the classification performance of ANN. Particularly, the PST-NN model can be used to predict and classify psychosocial risk levels among state-school teachers at work. This model could help to early identification of psychosocial risk and to the development of programs to prevent it. |
format | Online Article Text |
id | pubmed-8176537 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81765372021-06-16 Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers Mosquera Navarro, Rodolfo Castrillón, Omar Danilo Parra Osorio, Liliana Oliveira, Tiago Novais, Paulo Valencia, José Fernando PeerJ Comput Sci Artificial Intelligence BACKGROUND: Psychosocial risks, also present in educational processes, are stress factors particularly critical in state-schools, affecting the efficacy, stress, and job satisfaction of the teachers. This study proposes an intelligent algorithm to improve the prediction of psychosocial risk, as a tool for the generation of health and risk prevention assistance programs. METHODS: The proposed approach, Physical Surface Tension-Neural Net (PST-NN), applied the theory of superficial tension in liquids to an artificial neural network (ANN), in order to model four risk levels (low, medium, high and very high psychosocial risk). The model was trained and tested using the results of tests for measurement of the psychosocial risk levels of 5,443 teachers. Psychosocial, and also physiological and musculoskeletal symptoms, factors were included as inputs of the model. The classification efficiency of the PST-NN approach was evaluated by using the sensitivity, specificity, accuracy and ROC curve metrics, and compared against other techniques as the Decision Tree model, Naïve Bayes, ANN, Support Vector Machines, Robust Linear Regression and the Logistic Regression Model. RESULTS: The modification of the ANN model, by the adaptation of a layer that includes concepts related to the theory of physical surface tension, improved the separation of the subjects according to the risk level group, as a function of the mass and perimeter outputs. Indeed, the PST-NN model showed better performance to classify psychosocial risk level on state-school teachers than the linear, probabilistic and logistic models included in this study, obtaining an average accuracy value of 97.31%. CONCLUSIONS: The introduction of physical models, such as the physical surface tension, can improve the classification performance of ANN. Particularly, the PST-NN model can be used to predict and classify psychosocial risk levels among state-school teachers at work. This model could help to early identification of psychosocial risk and to the development of programs to prevent it. PeerJ Inc. 2021-05-26 /pmc/articles/PMC8176537/ /pubmed/34141875 http://dx.doi.org/10.7717/peerj-cs.511 Text en ©2021 Mosquera Navarro et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Mosquera Navarro, Rodolfo Castrillón, Omar Danilo Parra Osorio, Liliana Oliveira, Tiago Novais, Paulo Valencia, José Fernando Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers |
title | Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers |
title_full | Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers |
title_fullStr | Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers |
title_full_unstemmed | Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers |
title_short | Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers |
title_sort | improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8176537/ https://www.ncbi.nlm.nih.gov/pubmed/34141875 http://dx.doi.org/10.7717/peerj-cs.511 |
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