Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Mosquera Navarro, Rodolfo, Castrillón, Omar Danilo, Parra Osorio, Liliana, Oliveira, Tiago, Novais, Paulo, Valencia, José Fernando
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
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
_version_ 1783703275144478720
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
work_keys_str_mv AT mosqueranavarrorodolfo improvingclassificationbasedonphysicalsurfacetensionneuralnetforthepredictionofpsychosocialrisklevelinpublicschoolteachers
AT castrillonomardanilo improvingclassificationbasedonphysicalsurfacetensionneuralnetforthepredictionofpsychosocialrisklevelinpublicschoolteachers
AT parraosorioliliana improvingclassificationbasedonphysicalsurfacetensionneuralnetforthepredictionofpsychosocialrisklevelinpublicschoolteachers
AT oliveiratiago improvingclassificationbasedonphysicalsurfacetensionneuralnetforthepredictionofpsychosocialrisklevelinpublicschoolteachers
AT novaispaulo improvingclassificationbasedonphysicalsurfacetensionneuralnetforthepredictionofpsychosocialrisklevelinpublicschoolteachers
AT valenciajosefernando improvingclassificationbasedonphysicalsurfacetensionneuralnetforthepredictionofpsychosocialrisklevelinpublicschoolteachers