Detección automática de trastorno de ansiedad generalizada: un enfoque de estrategias de historia de vida

Ana Luisa Islas Avila; Alicia Martínez Rebollar; Ricardo Castrejón Salgado; Laura Ávila Jiménez; Hugo Estrada Esquivel

doi:10.17488/RMIB.46.SI-TAIH.1528

Authors

Ana Luisa Islas Avila Centro Nacional de Investigación y Desarrollo Tecnológico https://orcid.org/0009-0003-9172-6076
Alicia Martínez Rebollar ● Tecnológico Nacional de México/ Centro Nacional de Investigación y Desarrollo Tecnológico https://orcid.org/0000-0002-1071-8599
Ricardo Castrejón Salgado OOAD Morelos/ Instituto Mexicano del Seguro Social https://orcid.org/0000-0001-6463-844X
Laura Ávila Jiménez OOAD Morelos/ Instituto Mexicano del Seguro Social https://orcid.org/0000-0002-1388-3178
Hugo Estrada Esquivel ● Tecnológico Nacional de México/ Centro Nacional de Investigación y Desarrollo Tecnológico https://orcid.org/0000-0002-1466-7581

DOI:

https://doi.org/10.17488/RMIB.46.SI-TAIH.1528

Keywords:

classification, generalized anxiety disorder, life history strategies, machine learning

Abstract

This article presents a model for detecting generalized anxiety disorder (GAD) using supervised machine learning algorithms. A total of 244 records were analyzed, obtained through four questionnaires that assessed sociodemographic aspects, life history strategies, and anxiety levels. From these data, a subset of relevant features was selected, eight supervised algorithms were evaluated, and their hyperparameters were optimized. The XGBoost model showed the best performance in both unbalanced models and those using class balancing techniques. The results highlight the relevance of life history strategies as predictive variables, particularly in dimensions such as interpersonal relationships, early maturation, and impulsivity, which showed a significant association with the development of GAD. The main limitations of the study include the sample size and the focus on a population located in central Mexico. Nevertheless, the findings confirm the feasibility of integrating life history variables into predictive models of GAD. The XGBoost model implementing the SMOTE-Tomek Links balancing technique was validated and achieved an accuracy, sensitivity, precision, and F1 score of 0.85.

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