The role of main sociodemographic variables in suicide ideation detection using machine learning
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2025
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| _version_ | 1866901424045031424 |
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| author | Hernández-Chan, Gandhi Samuel Jiménez-Coello, Matilde Sosa-Correa, Manuel Vanega-Romero, Sally |
| author_facet | Hernández-Chan, Gandhi Samuel Jiménez-Coello, Matilde Sosa-Correa, Manuel Vanega-Romero, Sally |
| contents | <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17202717 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The role of main sociodemographic variables in suicide ideation detection using machine learning Hernández-Chan, Gandhi Samuel Jiménez-Coello, Matilde Sosa-Correa, Manuel Vanega-Romero, Sally sociodemographic mental health suicide ideation <p>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.</p> |
| title | The role of main sociodemographic variables in suicide ideation detection using machine learning |
| topic | sociodemographic mental health suicide ideation |
| url | https://doi.org/10.5281/zenodo.17202717 |