Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers

Rodolfo Mosquera Navarro; Omar Danilo Castrillón; Liliana Parra Osorio; Tiago Oliveira; Paulo Novais; José Fernando Valencia

doi:10.7717/peerj-cs.511

Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers

PeerJ Comput Sci. 2021 May 26:7:e511. doi: 10.7717/peerj-cs.511. eCollection 2021.

Authors

Rodolfo Mosquera Navarro^{1

2}, Omar Danilo Castrillón¹, Liliana Parra Osorio³, Tiago Oliveira⁴, Paulo Novais⁵, José Fernando Valencia⁶

Affiliations

¹ Departamento de Ingeniería Industrial, Universidad Nacional de Colombia, Manizales, Caldas, Colombia.
² Grupo Nuevas tecnologías trabajo y gestión, Universidad de San Buenaventura - Cali, Cali, Valle del Cauca, Colombia.
³ Centro de Investigaciones Socio jurídicas, Facultad de Derecho, Universidad Libre, Bogotá, Cundinamarca, Colombia.
⁴ Algoritmi Center, Universidade do Minho, Minho, Braga, Portugal.
⁵ Department of Informatics/Algoritmi Center, Universidade do Minho, Minho, Braga, Portugal.
⁶ Department of Ciencias y Tecnologías de la Información, Universidad de San Buenaventura - Cali, Cali, Valle del Cauca, Colombia.

Abstract

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.

Keywords: Artificial Intelligence; Classification; Neural network; Physical surface tension-neural net; Psychosocial risk; State-School teachers.

Grants and funding

The authors received financial support from the “Convocatoria Nacional para el Apoyo al Desarrollo de Tesis de Posgrado o de Trabajos Finales de Especialidades en el área de la Salud de la Universidad Nacional de Colombia 2017-2018” via Resolution 21 of december 2017 from Vicerectoria of Investigaciones, who selected the proposal research: “Sistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de Colombia”, with identification number Hermes 40976 and Quipu code 201010016754. The authors also received financial support from the “Universidad de San Buenaventura-Cali-Facultad de Ingeniería-Nuevas tecnologías trabajo y gestión”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.