Modelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombiana

Autores:: Campo-Yule, Jefferson-Eduardo
Díaz-Magé, Danny-Alberto
Ordoñez-Eraso, Hugo-Armando

Tipo de recurso:: Article of journal

Fecha de publicación:: 2025

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:

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dc.title.es-ES.fl_str_mv	Modelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombiana
title	Modelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombiana
spellingShingle	Modelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombiana Consumption Illegal psychoactive substances Machine learning Predictive model Consumo Modelo predictivo Sustancias psicoactivas ilegales
title_short	Modelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombiana
title_full	Modelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombiana
title_fullStr	Modelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombiana
title_full_unstemmed	Modelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombiana
title_sort	Modelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombiana
dc.creator.fl_str_mv	Campo-Yule, Jefferson-Eduardo Díaz-Magé, Danny-Alberto Ordoñez-Eraso, Hugo-Armando
dc.contributor.author.eng.fl_str_mv	Campo-Yule, Jefferson-Eduardo Díaz-Magé, Danny-Alberto Ordoñez-Eraso, Hugo-Armando
dc.subject.none.fl_str_mv	Consumption Illegal psychoactive substances Machine learning Predictive model Consumo Modelo predictivo Sustancias psicoactivas ilegales
topic	Consumption Illegal psychoactive substances Machine learning Predictive model Consumo Modelo predictivo Sustancias psicoactivas ilegales
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-07-05 15:50:32 2026-04-16T17:47:49Z
dc.date.available.none.fl_str_mv	2025-07-05 15:50:32
dc.date.issued.none.fl_str_mv	2025-07-05
dc.type.spa.fl_str_mv	Artículo de revista
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dc.relation.references.none.fl_str_mv	Ministerio de Justicia y Derecho, Situación del Consumo en Colombia, 2024. https://www.minjusticia.gov.co/programas-co/ODC/Paginas/Situaci%C3%B3n-consumo.aspx Ministerio de Salud, Prevención del consumo de Sustancias Psico-Activas (SPA) 2024. https://www.minsalud.gov.co/salud/publica/SMental/Paginas/convivencia-desarrollo-humano-sustancias-psicoactivas.aspx W. Ruiz Orejuela et al., Estudio nacional de consumo de sustancias psicoactivas Colombia, 2019. H. Gong, C. Xie, C. Yu, N. Sun, H. Lu, Y. Xie, “Psychosocial factors predict the level of substance craving of people with drug addiction: A machine learning approach,” International Journal of Environmental Research and Public Health, vol. 18, no. 22, e12175, Nov. 2021. https://doi.org/10.3390/ijerph182212175 U. I. Islam, I. H. Sarker, E. Haque, M. M. Hoque, “Predicting Individual Substance Abuse Vulnerability using Machine Learning Techniques,” Arxiv, Dec. 2020. http://arxiv.org/abs/2101.03184 M. A. I. Arif, S. I. Sany, F. Sharmin, M. S. Rahman, M. T. Habib, “Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population,” International Journal of Electrical and Computer Engineering, vol. 11, no. 5, pp. 4471-4480, Oct. 2021. https://doi.org/10.11591/ijece.v11i5.pp4471-4480 U. I. Islam, E. Haque, D. Alsalman, M. N. Islam, M. A. Moni, I. H. Sarker, “A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors,” Annals of Data Science, vol. 10, no. 6, pp. 1607–1634, 2022. https://doi.org/10.1007/s40745-022-00381-0 J. P. Davis et al., “Identifying individual and environmental predictors of opioid and psychostimulant use among adolescents and young adults following outpatient treatment,” Drug Alcohol Depend, vol. 233, e109359, Apr. 2022. https://doi.org/10.1016/j.drugalcdep.2022.109359 D. H. Han, D. C. Seo, “Identifying risk profiles for marijuana vaping among U.S. young adults by recreational marijuana legalization status: A machine learning approach,” Drug Alcohol Depend, vol. 232, e109330, Mar. 2022. https://doi.org/10.1016/j.drugalcdep.2022.109330 L. Zoboroski, T. Wagner, B. Langhals, “Classical and neural network machine learning to determine the risk of marijuana use,” International Journal of Environmental Research and Public Health, vol. 18, no. 14, e7466, Jul. 2021. https://doi.org/10.3390/ijerph18147466 S. Negriff, B. Dilkina, L. Matai, E. Rice, “Using machine learning to determine the shared and unique risk factors for marijuana use among child-welfare versus community adolescents,” PLoS One, vol. 17, no. 9, e0274998, Sep. 2022. https://doi.org/10.1371/journal.pone.0274998 K. Palomino, C. R. Berdugo, J. I. Vélez, “Leading consumption patterns of psychoactive substances in Colombia: A deep neural network-based clustering-oriented embedding approach,” PLoS One, vol. 18, no. 8, e0290098, Aug. 2023. https://doi.org/10.1371/journal.pone.0290098 H. Ordoñez, R. Timarán-Pereira, J.-S. González-Sanabria, “Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 9, n.o 3, pp. 52-60 2024. https://doi.org/10.9781/ijimai.2024.02.002 J. J. Pinto Hidalgo, J. A. Silva Centeno, “Geospatial Intelligence and Artificial Intelligence for Detecting Potential Coca Paste Production Infrastructure in the Border Region of Venezuela and Colombia,” Journal of Applied Security Research, vol. 18, no. 4, pp. 1000-1050, 2023. https://doi.org/10.1080/19361610.2022.2111184 C. Brito Carrillo, “Diagnostico prevención integral y evaluación de factores individuales, familiares y sociales asociados al uso y abuso de sustancias,” 2020, https://www.minjusticia.gov.co/programas-co/ODC/Publicaciones/Publicaciones/DIAGNOSTICO%20DE%20LA%20PROBLEMATICA%20DEL%20CONSUMO%20DE%20SUSTANCIAS%20PSICOACTIVAS%20DEPARTAMENTO%20DE%20LA%20GUAJIRA.pdf C. J. Brito Carrillo, E. E. Pinto Aragon, “Comprehensive attention in vulnerable sectors victims of the use and abuse of psychoactive substances in the juvenile penal responsibility processes law 1098 of 2006, in the department of La Guajira. Colombia,” Cultura, Educación y Sociedad, vol. 7, no. 1, pp. 104-116, Jan. 2016. https://doi.org/10.17981/cultedusoc.07.1.2016.8 Scikit-Learn, Grid Search CV, 2024. https://scikit-learn.org/0.18/modules/generated/sklearn.model_selection.GridSearchCV.html Scikit-Learn, Randomized Search CV, 2024. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html IBM, Funcionamiento de SVM, 2024. https://www.ibm.com/docs/es/spss-modeler/saas?topic=models-how-svm-works IBM, ¿Qué es KNN?, 2024. https://www.ibm.com/mx-es/topics/knn IBM, ¿Qué es un bosque aleatorio?, 2024. https://www.ibm.com/mx-es/topics/random-forest IBM, ¿Qué es boosting?, 2024. https://www.ibm.com/es-es/topics/boosting IBM, ¿Qué es la regresión logística?, 2024. https://www.ibm.com/mx-es/topics/logistic-regression IBM, ¿Qué es un árbol de decisión?, 2024. https://www.ibm.com/es-es/topics/decision-trees Scikit-Learn, Precision Score, 2024. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html Scikit-Learn, Metrics and scoring: quantifying the quality of predictions, 2024. https://scikit-learn.org/stable/modules/model_evaluation.html#accuracy-score Scikit-Learn, F1 Score, 2024. https://scikit-learn.org/0.15/modules/generated/sklearn.metrics.f1_score.html Scikit-Learn, Recall Score, 2024. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html
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dc.relation.bitstream.none.fl_str_mv	https://revistas.uptc.edu.co/index.php/ingenieria/article/download/19786/16035 https://revistas.uptc.edu.co/index.php/ingenieria/article/download/19786/15817
dc.relation.citationedition.eng.fl_str_mv	Núm. 72 , Año 2025 : April-June 2025 (Continuous Publication)
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spelling	Campo-Yule, Jefferson-EduardoDíaz-Magé, Danny-AlbertoOrdoñez-Eraso, Hugo-Armando2025-07-05 15:50:322026-04-16T17:47:49Z2025-07-05 15:50:322025-07-050121-1129https://repositorio.uptc.edu.co/handle/001/2161310.19053/uptc.01211129.v34.n72.2025.197862357-5328https://doi.org/10.19053/uptc.01211129.v34.n72.2025.19786text/xmlapplication/pdfUniversidad Pedagógica y Tecnológica de Colombiahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/19786ConsumptionIllegal psychoactive substancesMachine learningPredictive modelConsumoModelo predictivoSustancias psicoactivas ilegalesModelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombianaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85Ministerio de Justicia y Derecho, Situación del Consumo en Colombia, 2024. https://www.minjusticia.gov.co/programas-co/ODC/Paginas/Situaci%C3%B3n-consumo.aspxMinisterio de Salud, Prevención del consumo de Sustancias Psico-Activas (SPA) 2024. https://www.minsalud.gov.co/salud/publica/SMental/Paginas/convivencia-desarrollo-humano-sustancias-psicoactivas.aspxW. Ruiz Orejuela et al., Estudio nacional de consumo de sustancias psicoactivas Colombia, 2019.H. Gong, C. Xie, C. Yu, N. Sun, H. Lu, Y. Xie, “Psychosocial factors predict the level of substance craving of people with drug addiction: A machine learning approach,” International Journal of Environmental Research and Public Health, vol. 18, no. 22, e12175, Nov. 2021. https://doi.org/10.3390/ijerph182212175U. I. Islam, I. H. Sarker, E. Haque, M. M. Hoque, “Predicting Individual Substance Abuse Vulnerability using Machine Learning Techniques,” Arxiv, Dec. 2020. http://arxiv.org/abs/2101.03184M. A. I. Arif, S. I. Sany, F. Sharmin, M. S. Rahman, M. T. Habib, “Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population,” International Journal of Electrical and Computer Engineering, vol. 11, no. 5, pp. 4471-4480, Oct. 2021. https://doi.org/10.11591/ijece.v11i5.pp4471-4480U. I. Islam, E. Haque, D. Alsalman, M. N. Islam, M. A. Moni, I. H. Sarker, “A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors,” Annals of Data Science, vol. 10, no. 6, pp. 1607–1634, 2022. https://doi.org/10.1007/s40745-022-00381-0J. P. Davis et al., “Identifying individual and environmental predictors of opioid and psychostimulant use among adolescents and young adults following outpatient treatment,” Drug Alcohol Depend, vol. 233, e109359, Apr. 2022. https://doi.org/10.1016/j.drugalcdep.2022.109359D. H. Han, D. C. Seo, “Identifying risk profiles for marijuana vaping among U.S. young adults by recreational marijuana legalization status: A machine learning approach,” Drug Alcohol Depend, vol. 232, e109330, Mar. 2022. https://doi.org/10.1016/j.drugalcdep.2022.109330L. Zoboroski, T. Wagner, B. Langhals, “Classical and neural network machine learning to determine the risk of marijuana use,” International Journal of Environmental Research and Public Health, vol. 18, no. 14, e7466, Jul. 2021. https://doi.org/10.3390/ijerph18147466S. Negriff, B. Dilkina, L. Matai, E. Rice, “Using machine learning to determine the shared and unique risk factors for marijuana use among child-welfare versus community adolescents,” PLoS One, vol. 17, no. 9, e0274998, Sep. 2022. https://doi.org/10.1371/journal.pone.0274998K. Palomino, C. R. Berdugo, J. I. Vélez, “Leading consumption patterns of psychoactive substances in Colombia: A deep neural network-based clustering-oriented embedding approach,” PLoS One, vol. 18, no. 8, e0290098, Aug. 2023. https://doi.org/10.1371/journal.pone.0290098H. Ordoñez, R. Timarán-Pereira, J.-S. González-Sanabria, “Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 9, n.o 3, pp. 52-60 2024. https://doi.org/10.9781/ijimai.2024.02.002J. J. Pinto Hidalgo, J. A. Silva Centeno, “Geospatial Intelligence and Artificial Intelligence for Detecting Potential Coca Paste Production Infrastructure in the Border Region of Venezuela and Colombia,” Journal of Applied Security Research, vol. 18, no. 4, pp. 1000-1050, 2023. https://doi.org/10.1080/19361610.2022.2111184C. Brito Carrillo, “Diagnostico prevención integral y evaluación de factores individuales, familiares y sociales asociados al uso y abuso de sustancias,” 2020, https://www.minjusticia.gov.co/programas-co/ODC/Publicaciones/Publicaciones/DIAGNOSTICO%20DE%20LA%20PROBLEMATICA%20DEL%20CONSUMO%20DE%20SUSTANCIAS%20PSICOACTIVAS%20DEPARTAMENTO%20DE%20LA%20GUAJIRA.pdfC. J. Brito Carrillo, E. E. Pinto Aragon, “Comprehensive attention in vulnerable sectors victims of the use and abuse of psychoactive substances in the juvenile penal responsibility processes law 1098 of 2006, in the department of La Guajira. Colombia,” Cultura, Educación y Sociedad, vol. 7, no. 1, pp. 104-116, Jan. 2016. https://doi.org/10.17981/cultedusoc.07.1.2016.8Scikit-Learn, Grid Search CV, 2024. https://scikit-learn.org/0.18/modules/generated/sklearn.model_selection.GridSearchCV.htmlScikit-Learn, Randomized Search CV, 2024. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.htmlIBM, Funcionamiento de SVM, 2024. https://www.ibm.com/docs/es/spss-modeler/saas?topic=models-how-svm-worksIBM, ¿Qué es KNN?, 2024. https://www.ibm.com/mx-es/topics/knnIBM, ¿Qué es un bosque aleatorio?, 2024. https://www.ibm.com/mx-es/topics/random-forestIBM, ¿Qué es boosting?, 2024. https://www.ibm.com/es-es/topics/boostingIBM, ¿Qué es la regresión logística?, 2024. https://www.ibm.com/mx-es/topics/logistic-regressionIBM, ¿Qué es un árbol de decisión?, 2024. https://www.ibm.com/es-es/topics/decision-treesScikit-Learn, Precision Score, 2024. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.htmlScikit-Learn, Metrics and scoring: quantifying the quality of predictions, 2024. https://scikit-learn.org/stable/modules/model_evaluation.html#accuracy-scoreScikit-Learn, F1 Score, 2024. https://scikit-learn.org/0.15/modules/generated/sklearn.metrics.f1_score.htmlScikit-Learn, Recall Score, 2024. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.htmle1978672e1978634Revista Facultad de Ingenieríahttps://revistas.uptc.edu.co/index.php/ingenieria/article/download/19786/16035https://revistas.uptc.edu.co/index.php/ingenieria/article/download/19786/15817Núm. 72 , Año 2025 : April-June 2025 (Continuous Publication)PublicationOREORE.xmltext/xml2825https://repositorio.uptc.edu.co/bitstreams/fe6e302d-f798-4cf2-ac48-57d39cc521e5/downloadda206fe67a10124142701bee739ec222MD51falseAnonymousREAD001/21613oai:repositorio.uptc.edu.co:001/216132026-10-29 14:05:04.344https://creativecommons.org/licenses/by/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Modelo de regresión logística para predecir el nivel de riesgo de consumo o adicción a sustancias psicoactivas ilegales en población colombiana

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