Machine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombia

Disorder 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 rem...

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Autores:
Leon Jacobus, Alexandra
Ariza Colpas, Paola Patricia
Barcelo Martinez, Ernesto Alejandro
Piñeres-Melo, Marlon Alberto
Morales Ortega, Roberto
Ovallos-Gazabon, David Alfredo
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7290
Acceso en línea:
https://hdl.handle.net/11323/7290
https://doi.org/10.1007/978-3-030-47679-3_22
https://repositorio.cuc.edu.co/
Palabra clave:
ADHD disorder
Prevalence of symptoms
Pathology
Hyperactivity
Impulsivity
Classification techniques
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_e8b2b6d3f457206caef3f51ec90ee7ef
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7290
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Machine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombia
title Machine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombia
spellingShingle Machine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombia
ADHD disorder
Prevalence of symptoms
Pathology
Hyperactivity
Impulsivity
Classification techniques
title_short Machine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombia
title_full Machine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombia
title_fullStr Machine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombia
title_full_unstemmed Machine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombia
title_sort Machine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombia
dc.creator.fl_str_mv Leon Jacobus, Alexandra
Ariza Colpas, Paola Patricia
Barcelo Martinez, Ernesto Alejandro
Piñeres-Melo, Marlon Alberto
Morales Ortega, Roberto
Ovallos-Gazabon, David Alfredo
dc.contributor.author.spa.fl_str_mv Leon Jacobus, Alexandra
Ariza Colpas, Paola Patricia
Barcelo Martinez, Ernesto Alejandro
Piñeres-Melo, Marlon Alberto
Morales Ortega, Roberto
Ovallos-Gazabon, David Alfredo
dc.subject.spa.fl_str_mv ADHD disorder
Prevalence of symptoms
Pathology
Hyperactivity
Impulsivity
Classification techniques
topic ADHD disorder
Prevalence of symptoms
Pathology
Hyperactivity
Impulsivity
Classification techniques
description Disorder 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%.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-11-12T21:09:42Z
dc.date.available.none.fl_str_mv 2020-11-12T21:09:42Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/7290
https://doi.org/10.1007/978-3-030-47679-3_22
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dc.relation.references.spa.fl_str_mv 1. 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)
2. 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)
3. Cabanyes, J., García, D.: Trastorno por déficit de atención e hiperactividad en el adulto: perspectivas actuales. Psiquiatría Biol. 13(3), 86–94 (2006)
4. 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)
5. DANE: Archivo Nacional de Datos ANDA (2014). http://formularios.dane.gov.co/Anda_4_ 1/index.php/home. Citado 20 Marzo 2016
6. Pimienta-Lastra, R.: Encuestas probabilísticas vs. no probabilísticas. Polít. Cult. 13, 263–276 (2000)
7. 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)
8. 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 2016
9. 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)
10. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996). https://doi.org/10. 1007/BF00058655
11. Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)
12. 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_40
13. 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)
14. 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_21
15. 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 2006
16. Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)
17. 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)
18. Yih, W.T., Goodman, J., Hulten, G.: Learning at low false positive rates. In: CEAS, July 2006
19. 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 1997
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spelling Leon Jacobus, AlexandraAriza Colpas, Paola PatriciaBarcelo Martinez, Ernesto AlejandroPiñeres-Melo, Marlon AlbertoMorales Ortega, RobertoOvallos-Gazabon, David Alfredo2020-11-12T21:09:42Z2020-11-12T21:09:42Z20200302-9743https://hdl.handle.net/11323/7290https://doi.org/10.1007/978-3-030-47679-3_22Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Disorder 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%.Leon Jacobus, Alexandra-will be generated-orcid-0000-0003-3128-1274-600Ariza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600Barcelo Martinez, Ernesto Alejandro-will be generated-orcid-0000-0001-5881-4654-600Piñeres-Melo, Marlon AlbertoMorales Ortega, Roberto-will be generated-orcid-0000-0002-8219-9943-600Ovallos-Gazabon, David Alfredoapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Computer Sciencehttps://link.springer.com/chapter/10.1007/978-3-030-47679-3_22ADHD disorderPrevalence of symptomsPathologyHyperactivityImpulsivityClassification techniquesMachine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. 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)2. 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)3. Cabanyes, J., García, D.: Trastorno por déficit de atención e hiperactividad en el adulto: perspectivas actuales. Psiquiatría Biol. 13(3), 86–94 (2006)4. 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)5. DANE: Archivo Nacional de Datos ANDA (2014). http://formularios.dane.gov.co/Anda_4_ 1/index.php/home. Citado 20 Marzo 20166. Pimienta-Lastra, R.: Encuestas probabilísticas vs. no probabilísticas. Polít. Cult. 13, 263–276 (2000)7. 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)8. 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 20169. 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)10. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996). https://doi.org/10. 1007/BF0005865511. Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)12. 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_4013. 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)14. 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_2115. 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 200616. Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)17. 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)18. Yih, W.T., Goodman, J., Hulten, G.: Learning at low false positive rates. In: CEAS, July 200619. 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 1997PublicationORIGINALMachine Learning Approach Applied.pdfMachine Learning Approach Applied.pdfapplication/pdf845463https://repositorio.cuc.edu.co/bitstreams/71e26843-ec9f-4ea2-bd5b-4264b00fb531/download67b9d075d76e1e3f23220113e8cb03b9MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/9fd23dfc-05cc-4dd2-8975-6786f4526bcf/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/7d889d9e-f115-4580-9d09-cb13c3790af3/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILMachine Learning Approach Applied.pdf.jpgMachine Learning Approach Applied.pdf.jpgimage/jpeg39263https://repositorio.cuc.edu.co/bitstreams/5e41c66e-7d9f-4834-99da-e3871dc9dbae/downloadfb8db39442ae866c4e0250260ee1a0e4MD54TEXTMachine Learning Approach 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