Show simple item record

dc.creatorLeon Jacobus, Alexandra
dc.creatorAriza Colpas, Paola Patricia
dc.creatorBarcelo Martinez, Ernesto Alejandro
dc.creatorPiñeres-Melo, Marlon Alberto
dc.creatorMorales Ortega, Roberto
dc.creatorOvallos-Gazabon, David Alfredo
dc.date.accessioned2020-11-12T21:09:42Z
dc.date.available2020-11-12T21:09:42Z
dc.date.issued2020
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11323/7290
dc.description.abstractDisorder Attention Deficit/Hyperactivity Disorder, or ADHD, is recognized as one of the pathologies of high prevalence in children and adolescents from the global environment population; this disorder generates visible symptoms usually diminish with the passage of time in adulthood, however they remain concealed by demonstrations damnifican personal stability and human development apt. This article shows the results of the research aimed at determining the prevalence of symptoms of attention deficit hyperactivity disorder in Young Adults University of Barranquilla and its Metropolitan Area. The sample of 1600 young adults between 18 and 25 years, which has been estimated at 95% confidence level and a margin of error of 2.44%. The information was acquired through the application of exploratory instruments symptoms of attention deficit hyperactivity disorder. With the application of the algorithm different machine learning algorithms such as: Bagging, MultiBoostAB, DecisionStump, LogitBoost, FT, J48Graft, a high performance in the Bagging algorithm could be identified with the following results in quality metrics: Accuracy 91.67%, Precision 94.12%, Recall 88.89% and F-measure 91.43%.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceLecture Notes in Computer Sciencespa
dc.subjectADHD disorderspa
dc.subjectPrevalence of symptomsspa
dc.subjectPathologyspa
dc.subjectHyperactivityspa
dc.subjectImpulsivityspa
dc.subjectClassification techniquesspa
dc.titleMachine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombiaspa
dc.typearticlespa
dcterms.references1. Velázquez, J., García, M.: Trastorno por déficit de la atención e hiperactividad de la infancia a la vida adulta. Red de Revistas Científicas de América Latina, el Caribe, España y Portugal 9(4), 176–181 (2007)spa
dcterms.references2. Ramos-Quiroga, J., Chalita, P., Vidal, R., Bosch, R., Palomar, G., et al.: Diagnóstico y tratamiento del trastorno por déficit de atención/hiperactividad en adultos. Rev. Neurol. 54 (1), 105–115 (2000)spa
dcterms.references3. Cabanyes, J., García, D.: Trastorno por déficit de atención e hiperactividad en el adulto: perspectivas actuales. Psiquiatría Biol. 13(3), 86–94 (2006)spa
dcterms.references4. Faraone, S.V., Biederman, J., Spencer, T., Wilens, T., Seidman, L.J., et al.: Attentiondeficit/hyperactivity disorder in adults: an overview. Biol. Psychiatry 48(1), 9–20 (2000)spa
dcterms.references5. DANE: Archivo Nacional de Datos ANDA (2014). http://formularios.dane.gov.co/Anda_4_ 1/index.php/home. Citado 20 Marzo 2016spa
dcterms.references6. Pimienta-Lastra, R.: Encuestas probabilísticas vs. no probabilísticas. Polít. Cult. 13, 263–276 (2000)spa
dcterms.references7. León-Jacobus, A., Valle-Cordoba, S., Florez-Niño, Y.: Diseño y validación piloto del inventario exploratorio de síntomas de TDAH (IES-TDAH) ajustado al DSM-V en jóvenes universitarios (Trabajo de Grado) (2007)spa
dcterms.references8. Adler, L., Kessler, R., Spencer, T.: Instrucciones para contestar la Escala de Auto-reporte de síntomas de TDAH en Adultos (ASRS-V1.1) (2003). http://www.neuropediatrica.com/ descargas/tests/AUTOREPORTE%20TDA%20ADUL.pdf. Citado 15 Feb 2016spa
dcterms.references9. Barceló-Martínez, E., León-Jacobus, A., Cortes-Peña, O., Valle-Córdoba, S., Flórez-Niño, Y.: Validación del inventario exploratorio de síntomas de TDAH (IES-TDAH) ajustado al DSM-V. Rev. Mex. Neu. 17(1), 1–113 (2016)spa
dcterms.references10. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996). https://doi.org/10. 1007/BF00058655spa
dcterms.references11. Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)spa
dcterms.references12. Pang, J., Huang, Q., Jiang, S.: Multiple instance boost using graph embedding based decision stump for pedestrian detection. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 541–552. Springer, Heidelberg (2008). https://doi.org/10.1007/ 978-3-540-88693-8_40spa
dcterms.references13. Bhargava, N., Sharma, G., Bhargava, R., Mathuria, M.: Decision tree analysis on J48 algorithm for data mining. Proc. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(6) (2013)spa
dcterms.references14. Ariza-Colpas, P., et al.: Enkephalon - technological platform to support the diagnosis of Alzheimer’s disease through the analysis of resonance images using data mining techniques. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2019. LNCS, vol. 11656, pp. 211–220. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26354-6_21spa
dcterms.references15. Davis, J., Goadrich, M.: The relationship between Precision-Recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240, June 2006spa
dcterms.references16. Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)spa
dcterms.references17. Ye, K., Anton Feenstra, K., Heringa, J., IJzerman, A.P., Marchiori, E.: Multi-RELIEF: a method to recognize specificity determining residues from multiple sequence alignments using a Machine-Learning approach for feature weighting. Bioinformatics 24(1), 18–25 (2008)spa
dcterms.references18. Yih, W.T., Goodman, J., Hulten, G.: Learning at low false positive rates. In: CEAS, July 2006spa
dcterms.references19. Lane, T., Brodley, C.E.: An application of machine learning to anomaly detection. In: Proceedings of the 20th National Information Systems Security Conference, Baltimore, USA, vol. 377, pp. 366–380, October 1997spa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-3-030-47679-3_22spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-3-030-47679-3_22


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal