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Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

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  • Svajone Bekesiene

    (General Jonas Zemaitis Military Academy of Lithuania, Silo 5a, 10322 Vilnius, Lithuania)

  • Rasa Smaliukiene

    (General Jonas Zemaitis Military Academy of Lithuania, Silo 5a, 10322 Vilnius, Lithuania)

  • Ramute Vaicaitiene

    (General Jonas Zemaitis Military Academy of Lithuania, Silo 5a, 10322 Vilnius, Lithuania)

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.

Suggested Citation

  • Svajone Bekesiene & Rasa Smaliukiene & Ramute Vaicaitiene, 2021. "Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts," Mathematics, MDPI, vol. 9(6), pages 1-23, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:626-:d:517654
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    References listed on IDEAS

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    1. Song, Qingbin & Li, Jinhui & Duan, Huabo & Yu, Danfeng & Wang, Zhishi, 2017. "Towards to sustainable energy-efficient city: A case study of Macau," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 504-514.
    2. Sea-Jin Chang & Arjen van Witteloostuijn & Lorraine Eden, 2010. "From the Editors: Common method variance in international business research," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 41(2), pages 178-184, February.
    3. Rasa Smaliukiene & Svajone Bekesiene, 2020. "Towards Sustainable Human Resources: How Generational Differences Impact Subjective Wellbeing in the Military?," Sustainability, MDPI, vol. 12(23), pages 1-21, November.
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    1. Juliana Mio de Souza & Paulo Morgado & Eduarda Marques da Costa & Luiz Fernando de Novaes Vianna, 2022. "Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    2. José Javier Galán & Ramón Alberto Carrasco & Antonio LaTorre, 2022. "Military Applications of Machine Learning: A Bibliometric Perspective," Mathematics, MDPI, vol. 10(9), pages 1-27, April.

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