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Exploring emotional stability: from conventional approaches to machine learning insights

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Abstract

In contemporary psychological assessments, diverse traits are often evaluated using extensive questionnaires. This study focuses on the trait of emotional stability, and acknowledges the inherent limitations and issues associated with prolonged survey instruments. To address these challenges, we propose a Machine Learning (ML) approach to directly predict emotional stability, offering a more efficient alternative to bulky questionnaires. The study carefully selected variables with previously established relationships to emotional stability, utilizing a dataset of 2203 individuals who responded to a series of psychometric questionnaires. The proposed method yields promising results, achieving an R2 score of approximately 0.71 on the test set, indicating robust predictive performance. These models highlighted the significance of variables such as emotional stress and self-esteem, emphasizing their substantial role in predicting emotional stability. It is noteworthy that even with a reduced set of variables, the models remained statistically equivalent. The results provide valuable insights for predicting stability with smaller sets of variables and contribute knowledge that complements the understanding of emotional stability.

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Data availability

Data available upon request to the corresponding author.

Abbreviations

BR:

Bayesian Ridge

DT:

Decision Tree

GB:

Gradient Boosting

k-NN:

K-Nearest Neighbour

LR:

Linear Regression

MLP:

Multi-Layer Perceptron

MSE:

Mean Squared Error

R 2 :

R-squared

RF:

Random Forest

SVR:

Support Vector Regression

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Acknowledgements

The authors extend their heartfelt gratitude to RH Asesores Improving, Talent Development Institute for its invaluable contributions and support throughout the research process. Furthermore, we wish to emphasize that this research was undertaken without specific funding, and the authors conducted and concluded the study independently.

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, 18014, Granada, Andalucía, Spain

    Marcos Romero Madroñal & María del Carmen Pegalajar Jiménez

  2. Faculty of Health Sciences, Universidad Villanueva, Calle Costa Brava 2, 28034, Madrid, Spain

    Eduar S. Ramírez

  3. Department of Software Engineering, University of Granada, 18014, Granada, Andalucía, Spain

    Luis Gonzaga Baca Ruiz

  4. Department of Psychology, Rovira I Virgili University, 43002, Tarragona, Spain

    María José Serrano-Fernández

  5. RH Asesores Improving S.L, Instituto Para El Desarrollo del Talento Natural y El Nuevo Liderazgo, 28221, Madrid, Spain

    Elena Pérez-Moreiras

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  1. Marcos Romero Madroñal
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Correspondence to María del Carmen Pegalajar Jiménez.

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Madroñal, M.R., Ramírez, E.S., Ruiz, L.G.B. et al. Exploring emotional stability: from conventional approaches to machine learning insights. Appl Intell 55, 213 (2025). https://doi.org/10.1007/s10489-024-06130-5

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