Journal of Artificial Intelligence and Computing Applications (2025) - Special Issue - 3(2): 11 Conference abstract The role of main sociodemographic variables in suicide ideation detection using machine learning Gandhi Samuel Hernandez-Chan 1,*, Matilde Jim´enez-Coello 2, Manuel Sosa-Correa 2, and Sally Vanega-Romero 2 1Centro de Investigaci´on en Ciencias de Informaci´on Geoespacial 2Universidad Aut´onoma de Yucat´an ABSTRACT This study investigates the role of key sociodemographic variables in the detection of suicidal ideation within a Yucatan population, employing machine learning (ML) classification models. Data was obtained from the APPSI platform, encompassing psychological assessments (C-SSRS, DASS21, EAYIE) and sociodemographic information (age, gender, municipality, etc.). Ten classification algorithms were trained and evaluated to predict suicidal ideation. The Logistic Regression model demonstrated the strongest performance, achieving an F1 score of 0.77 and an accuracy of 81%, with a recall of 74% for the at-risk group. Results highlight the potential of ML to support mental health decision-making and the importance of sociodemographic factors in suicide ideation detection. Keywords: sociodemographic, mental health, suicide ideation This work corresponds to a paper presented at the International Conference on Artificial Intelligence for Mental Health (ICAIMH) 2025. The complete version is expected to be published in the Journal of Artificial Intelligence and Computing Applications (JAICA) and will be available at: https://www.maikron.org/jaica/. E-mail address:
[email protected] https://doi.org/10.5281/zenodo.17202717 ©2025 The Author(s). Published by Maikron. This is an open access article under the CC BY license. This article is part of the Special Issue on ICAIMH 2025. ISSN: 3061-8843
