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.

中文翻译：

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基于物理表面张力神经网络的改进分类用于预测公立学校教师的心理社会风险水平


背景心理社会风险也存在于教育过程中，是公立学校尤其重要的压力因素，影响教师的效能、压力和工作满意度。本研究提出了一种智能算法来改进心理社会风险的预测，作为生成健康和风险预防援助计划的工具。方法所提出的方法物理表面张力神经网络（PST-NN）将液体表面张力理论应用于人工神经网络（ANN），以模拟四个风险级别（低、中、高和极高）社会心理风险）。该模型使用测量 5,443 名教师心理社会风险水平的测试结果进行了训练和测试。心理社会以及生理和肌肉骨骼症状因素都被纳入模型的输入。通过使用灵敏度、特异性、准确性和 ROC 曲线指标来评估 PST-NN 方法的分类效率，并与决策树模型、朴素贝叶斯、人工神经网络、支持向量机、鲁棒线性回归和逻辑回归等其他技术进行比较回归模型。结果 通过调整包含与物理表面张力理论相关的概念的层，对 ANN 模型进行了修改，根据风险级别组改进了对象的分离，作为质量和周长输出的函数。事实上，PST-NN 模型在对公立学校教师的心理社会风险水平进行分类方面表现出比本研究中的线性模型、概率模型和逻辑模型更好的性能，获得了 97.31% 的平均准确率。 结论物理表面张力等物理模型的引入可以提高人工神经网络的分类性能。特别是，PST-NN 模型可用于预测和分类公立学校教师在职中的心理社会风险水平。该模型有助于及早识别社会心理风险并制定预防方案。