Abstract
The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.
Highlights
The demand for artificial intelligence in human stress management is growing with a better understanding of the damage to human health caused by chronic stress
The different functions were used for different layers: the activation function was used as the hyperbolic tangent for the hidden layer, and the softmax function was used for the output layer
This indicated that the ANN2 model, in which data were divided for training, testing, and holdout by 60%–20%–20%, respectively, with hyperbolic tangent used as the activation function in the hidden layer and the softmax function used in the output layer, yielded a better validation result and proved the model’s high capability to predict the perceived level of stress in the three layers
Summary
The demand for artificial intelligence in human stress management is growing with a better understanding of the damage to human health caused by chronic stress. Stress may lead to the development of a post-traumatic stress disorder [4], and considerably reduce the quality of life [5]. In response to these findings, researchers have already developed different methods for measuring stress and presented different approaches for the causes of stress. Artificial intelligence needs to be used to analyze diverse stress stressors that are measured in a stressful environment, such as the beginning of military conscription service. The start of conscription is marked by “stressors of social experience” [6] due to the specific nature of the situation, such as distancing from family and friends, being in a masculine-warrior narrative [7], having a strict and busy daily routine [8], and doing physically-demanding tasks
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