Decision tree as a screening tool for the diagnosis of the metabolic syndrome without doing a blood test
Metabolic Syndrome (MetS) is a pathology with a high probability of triggering the onset of diabetes, coronary heart disease, and other diseases. So, the more the data is analyzed, the closer it will be to finding a way to prevent or delay the occurrence of its atrocious results. Thus, the medical c...
- Autores:
-
Barrios, Mauricio
Jimeno, Miguel
Navarro, Edgar
Villalba, Pedro
Hernandez, Yamid
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13650
- Acceso en línea:
- https://hdl.handle.net/11323/13650
https://repositorio.cuc.edu.co/
- Palabra clave:
- Decision Tree
Diagnostic non-invasive
Metabolic syndrome
Random subsampling validation
Screening Tool
- Rights
- closedAccess
- License
- Atribución-CompartirIgual 4.0 Internacional (CC BY-SA 4.0)
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Decision tree as a screening tool for the diagnosis of the metabolic syndrome without doing a blood test |
| title |
Decision tree as a screening tool for the diagnosis of the metabolic syndrome without doing a blood test |
| spellingShingle |
Decision tree as a screening tool for the diagnosis of the metabolic syndrome without doing a blood test Decision Tree Diagnostic non-invasive Metabolic syndrome Random subsampling validation Screening Tool |
| title_short |
Decision tree as a screening tool for the diagnosis of the metabolic syndrome without doing a blood test |
| title_full |
Decision tree as a screening tool for the diagnosis of the metabolic syndrome without doing a blood test |
| title_fullStr |
Decision tree as a screening tool for the diagnosis of the metabolic syndrome without doing a blood test |
| title_full_unstemmed |
Decision tree as a screening tool for the diagnosis of the metabolic syndrome without doing a blood test |
| title_sort |
Decision tree as a screening tool for the diagnosis of the metabolic syndrome without doing a blood test |
| dc.creator.fl_str_mv |
Barrios, Mauricio Jimeno, Miguel Navarro, Edgar Villalba, Pedro Hernandez, Yamid |
| dc.contributor.author.none.fl_str_mv |
Barrios, Mauricio Jimeno, Miguel Navarro, Edgar Villalba, Pedro Hernandez, Yamid |
| dc.subject.proposal.eng.fl_str_mv |
Decision Tree Diagnostic non-invasive Metabolic syndrome Random subsampling validation Screening Tool |
| topic |
Decision Tree Diagnostic non-invasive Metabolic syndrome Random subsampling validation Screening Tool |
| description |
Metabolic Syndrome (MetS) is a pathology with a high probability of triggering the onset of diabetes, coronary heart disease, and other diseases. So, the more the data is analyzed, the closer it will be to finding a way to prevent or delay the occurrence of its atrocious results. Thus, the medical community is doing prevalence studies in several populations to analyze it. However, the cost of doing a blood test increases the budget. This article presents a screening tool to diagnose the MetS without doing a blood test using a decision tree model with the data of 615 subjects of a study conducted in Colombia anthropometric. We created a new decision tree and used the random subsampling technique to validate the proposed model to compare with other decision trees found in the art of state process. We proposed an excellent decision tree with the best Area under Receiver Operating Characteristic (AROC) of 82,58% that uses sex, waist perimeter, hip perimeter, systolic pressure, and diastolic pressure variables to predict the Metabolic Syndrome's diagnosis with the Harmonized Metabolic Syndrome. standard. Therefore, we invite using this model as a screening tool to diagnose the MetS early in a medical consultation or prevalence study. © 2023, Centre for Environment and Socio-Economic Research Publications. All rights reserved. |
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2023 |
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2023 |
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2024-11-08T15:07:56Z |
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2024-11-08T15:07:56Z |
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Artículo de revista |
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Barrios, M., Jimeno, M., Navarro, E., Villalba, P., & Hernandez, Y. (2023). Decision Tree as a screening tool for the diagnosis of the Metabolic Syndrome without doing a blood test. International Journal of Artificial Intelligence, 21(1), 109-131. |
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0974-0635 |
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https://hdl.handle.net/11323/13650 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Barrios, M., Jimeno, M., Navarro, E., Villalba, P., & Hernandez, Y. (2023). Decision Tree as a screening tool for the diagnosis of the Metabolic Syndrome without doing a blood test. International Journal of Artificial Intelligence, 21(1), 109-131. 0974-0635 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/13650 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartofjournal.none.fl_str_mv |
International Journal of Artificial Intelligence |
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Aguilar, M., Bhuket, T., Torres, S., Liu, B., Wong, R. J. Prevalence of the metabolic syndrome in the united states, 2003-2012 (2015) JAMA, 313, p. 1973. Alberti, K., Eckel, R. H., Grundy, S. M., Zimmet, P. Z., Cleeman, J. I., Donato, K. A., Fruchart, J.-C., Smith, S. C. Harmonizing the metabolic syndrome (2009) Circulation, 120, pp. 1640-1645. Alberti, K. G. M. M., Zimmet, P., Shaw, J., George, . K., Alberti, M. M., Aschner, P., Balkau, B., Unwin, N. Metabolic syndrome-a new world-wide definition. a consensus statement from the international diabetes federation (2006), Alberti, K. G. M. M., Zimmet, P Z. (1997) Definition, diagnosis and classification of diabetes mellitus and its complications part 1: Diagnosis and classification of diabetes mellitus provisional report of a who consultation original articles, Aschner, P. Metabolic syndrome as a risk factor for diabetes (2010) Expert Review of Cardiovascular Therapy, 8, pp. 407-412. Asman, S. H., Aziz, N. F. A., Amirulddin, U. A. U., Kadir, M. Z. A. A. Decision tree method for fault causes classification based on rms-dwt analysis in 275 kv transmission lines network (2021) Applied Sciences, 11, p. 4031. Barengo, N. C., Katoh, S., Moltchanov, V., Tajima, N., Tuomilehto, J. The diabetes-cardiovascular risk paradox: Results from a finnish population-based prospective study (2008) European Heart Journal, 29, pp. 1889-1895. Barrios, M., Jimeno, M., Villalba, P, Navarro, E. Novel Data Mining Methodology for Healthcare Applied to a New Model to Diagnose Metabolic Syndrome without a Blood Test (2019) Diagnostics, 9 (4), p. 192. Barrios, M., Jimeno, M., Villalba, P, Navarro, E. Framework to diagnose the metabolic syndrome types without using a blood test based on machine learning (2020) Applied Sciences, 10, p. 8404. (2017) National Diabetes Statistics Report, 2017, p. 2009. Centers for Disease Control and Prevention US Department of Health and Human Services (Cdc): 2012 Chen, H., Xiong, S., Ren, X. Evaluating the risk of metabolic syndrome based on an artificial intelligence model (2014) Abstract and Applied Analysis, 2014, pp. 1-12. Chobanian, A. V., Bakris, G. L., Black, H. R., Cushman, W. C., Green, L. A., Izzo, J. L., Jones, D. W., Roccella, E. J. Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure (2003) Hypertension, 42, pp. 1206-1252. Cleophas, T. J., Zwinderman, A. H. (2015) Machine Learning in Medicine - a Complete Overview, Springer International Publishing Cornier, M.-A., Dabelea, D., Hernandez, T. L., Lindstrom, R. C., Steig, A. J., Stob, N. R., Pelt, R. E. V., Eckel, R. H. The metabolic syndrome (2008) Endocrine Reviews, 29, pp. 777-822. Datta, S., Schraplau, A., da Cruz, H. F, Sachs, J. P., Mayer, F., Bottinger, E. A machine learning approach for non-invasive diagnosis of metabolic syndrome (2019) IEEE, pp. 933-940. Deshmukh, P., Garg, N., Goswami, K., Bhalavi, V. Prevalence and correlates of metabolic syndrome in the adolescents of rural wardha (2015) Indian Journal of Community Medicine, 40, p. 43. Díaz-Apodaca, B. A., Ebrahim, S., McCormack, V., de Cosío, F. G., Ruiz-Holguín, R. Prevalence of type 2 diabetes and impaired fasting glucose: Cross-sectional study of multiethnic adult population at the united states-mexico border (2010) Revista Panamericana de Salud Publica, 28, pp. 174-181. Esposito, K., Chiodini, P., Colao, A., Lenzi, A., Giugliano, D. Metabolic syndrome and risk of cancer: A systematic review and meta-analysis (2012) Diabetes Care, 35, pp. 2402-2411. Executive summary of the third report of the national cholesterol education program (ncep) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel iii) (2001) JAMA: The Journal of the American Medical Association, 285, pp. 2486-2497. Ford, E. S., Giles, W. H., Dietz, W. H. Prevalence of the metabolic syndrome among us adults (2002) JAMA, 287, p. 356. Grundy, S. M. Metabolic syndrome: Connecting and reconciling cardiovascular and diabetes worlds (2006) Journal of the American College of Cardiology, 47, pp. 1093-1100. Grundy, S. M. Metabolic syndrome: A multiplex cardiovascular risk factor (2007) Journal of Clinical Endocrinology and Metabolism, 92, pp. 399-404. Grundy, S. M. Metabolic syndrome pandemic (2008) Arteriosclerosis, Thrombosis, and Vascular Biology, 28, pp. 629-636. Grundy, S. M. Pre-diabetes, metabolic syndrome, and cardiovascular risk (2012) Journal of the American College of Cardiology, 59, pp. 635-643. Gurkova, E., Čáp, J., Žiaková, K. Quality of life and treatment satisfaction in the context of diabetes self-management education (2009) International Journal of Nursing Practice, 15, pp. 91-98. Gutiérrez-Solis, A. L., Banik, S. D., Méndez-González, R. M. Prevalence of metabolic syndrome in mexico: A systematic review and meta analysis (2018) Metabolic Syndrome and Related Disorders, 16, pp. 395-405. Hosmer, J. D. W., Lemeshow, S. (2004) Applied Logistic Regression, John Wiley Sons Hu, F. B. Sedentary lifestyle and risk of obesity and type 2 diabetes (2003) Lipids, 38, pp. 103-108. Irving, G., Neves, A. L., Dambha-Miller, H., Oishi, A., Tagashira, H., Verho, A., Holden, J. International variations in primary care physician consultation time: A systematic review of 67 countries (2017) BMJ Open, 7, p. E017902. Jover, A., Corbella, E., Mun, A., Pedro-botet, J., Herna, A., Zu, M. (2011) Prevalence of metabolic syndrome and its components in patients with acute coronary syndrome, 64, pp. 579-586. Karimi-alavijeh, F., Jalili, S., Sadeghi, M. Predicting metabolic syndrome using decision tree and support vector machine methods (2016) ARYA Atheroscler, 12, pp. 146-152. Kaur, J. A Comprehensive Review on Metabolic Syndrome (2014) Cardiology Research and Practice, 2014, pp. 1-21. Http://dx.doi.org http://www.hindawi.com/journals/crp/2014/943162 Kolb, H., Martin, S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes (2017) BMC Medicine, 15, p. 131. Kroon, M. L. A., Renders, C. M., Kuipers, E. C. C., van Wouwe, J. P., van Buuren, S., de Jonge, G. A., Hirasing, R. A. Identifying metabolic syndrome without blood tests in young adults–the terneuzen birth cohort (2008) The European Journal of Public Health, 18, pp. 656-660. Linoff, G. S., Berry, M. J. A. (2011) Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd edn, Wiley Publishing Marchi-Alves, L. M., Rigotti, A. R., Nogueira, M. S., Cesarino, C. B., de Godoy, S. Componentes da sindrome metabólica na hipertensao arterial (2012) Revista da Escola de Enfermagem da USP, 46, pp. 1348-1353. Miller, B., Fridline, M., Liu, P.-Y., Marino, D. Use of chaid decision trees to formulate pathways for the early detection of metabolic syndrome in young adults (2014) Computational and Mathematical Methods in Medicine, 2014, pp. 1-7. (2015) Informe nacional de calidad de la atención en salud 2015, p. 217. Minsalud Morse, S. A., Zhang, R., Thakur, V., Reisin, E. Hypertension and the metabolic syndrome (2005) The American Journal of the Medical Sciences, 330, pp. 303-310. Mozumdar, A., Liguori, G. Persistent increase of prevalence of metabolic syndrome among u.s. adults: Nhanes iii to nhanes 1999-2006 (2011) Diabetes Care, 34, pp. 216-219. Muller-Nordhorn, J., Willich, S. N. (2017) Coronary heart disease, Navarro, E., Vargas, R. F. Síndrome metabólico en el suroccidente de barranquilla (2008) Revista Científica Salud Uninorte, 24, pp. 1-10. Navarro, E., Vargas, R. F. Riesgo coronario según ecuación de framingham en adultos con síndrome metabólico de la ciudad de soledad, atlantico (2012) 2010, Revista Colombiana de Cardiología, 19, pp. 109-118. Navarro, E., Vargas, R. F. Grasa corporal total como posible indicador de síndrome metabólico en adultos (2016) Revista Española de Nutrición Humana y Dietética, 20, p. 198. Perveen, S., Shahbaz, M., Keshavjee, K., Guergachi, A. Metabolic syndrome and development of diabetes mellitus: Predictive modeling based on machine learning techniques (2019) IEEE Access, 7, pp. 1365-1375. Rao, C., Wu, Y. Linear model selection by cross-validation (2005) Journal of Statistical Planning and Inference, 128, pp. 231-240. Romero-Saldana, M., Fuentes-Jimenez, F. J., Vaquero-Abellán, M., Álvarez Fernández, C., Molina-Recio, G., López-Miranda, J. New non-invasive method for early detection of metabolic syndrome in the working population (2016) European Journal of Cardiovascular Nursing, 15, pp. 549-558. Romero-Saldaña, M., Tauler, P., Vaquero-Abellán, M., López-González, A.-A., Fuentes Jiménez, F.-J., Aguiló, A., Álvarez Fernández, C., Bennasar-Veny, M. Validation of a non-invasive method for the early detection of metabolic syndrome: A diagnostic accuracy test in a working population (2018) BMJ Open, 8, p. E020476. Ruckstieß, T., Osendorfer, C., van der Smagt, P. (2011) Sequential feature selection for classification, Schargrodsky, H., Hernández-Hernández, R., Champagne, B. M., Silva, H., Vinueza, R., Aycaguer, L. C. S., Touboul, P-J., Wilson, E. Carmela: Assessment of cardiovascular risk in seven latin american cities (2008) The American Journal of Medicine, 121, pp. 58-65. Uusitupa, M., Khan, T. A., Viguiliouk, E., Kahleova, H., Rivellese, A. A., Hermansen, K., Pfeiffer, A., Sievenpiper, J. L. Prevention of type 2 diabetes by lifestyle changes: A systematic review and meta analysis (2019) Nutrients, 11, p. 2611. Comment on the provisional report from the who consultation (1999) Diabetic Medicine, 16, pp. 442-443. Witten, I. H., Frank, E. (2005) Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems), Morgan Kaufmann Publishers Inc Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., Pidetcha, P., Prachayasittikul, V. Identification of metabolic syndrome using decision tree analysis (2010) Diabetes Research and Clinical Practice, 90, pp. E15-e18. Xu, Q.-S., Liang, Y.-Z. Monte carlo cross validation (2001) Chemometrics and Intelligent Laboratory Systems, 56, pp. 1-11. |
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Atribución-CompartirIgual 4.0 Internacional (CC BY-SA 4.0)© 2023, Centre for Environment and Socio-Economic Research Publications. All rights reserved.https://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbBarrios, MauricioJimeno, MiguelNavarro, EdgarVillalba, PedroHernandez, Yamid2024-11-08T15:07:56Z2024-11-08T15:07:56Z2023Barrios, M., Jimeno, M., Navarro, E., Villalba, P., & Hernandez, Y. (2023). Decision Tree as a screening tool for the diagnosis of the Metabolic Syndrome without doing a blood test. International Journal of Artificial Intelligence, 21(1), 109-131.0974-0635https://hdl.handle.net/11323/13650Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Metabolic Syndrome (MetS) is a pathology with a high probability of triggering the onset of diabetes, coronary heart disease, and other diseases. So, the more the data is analyzed, the closer it will be to finding a way to prevent or delay the occurrence of its atrocious results. Thus, the medical community is doing prevalence studies in several populations to analyze it. However, the cost of doing a blood test increases the budget. This article presents a screening tool to diagnose the MetS without doing a blood test using a decision tree model with the data of 615 subjects of a study conducted in Colombia anthropometric. We created a new decision tree and used the random subsampling technique to validate the proposed model to compare with other decision trees found in the art of state process. We proposed an excellent decision tree with the best Area under Receiver Operating Characteristic (AROC) of 82,58% that uses sex, waist perimeter, hip perimeter, systolic pressure, and diastolic pressure variables to predict the Metabolic Syndrome's diagnosis with the Harmonized Metabolic Syndrome. standard. Therefore, we invite using this model as a screening tool to diagnose the MetS early in a medical consultation or prevalence study. © 2023, Centre for Environment and Socio-Economic Research Publications. All rights reserved.23 páginasapplication/pdfengCentre for Environment and Socio-Economic Research PublicationsIndiahttps://www.scopus.com/record/display.uri?eid=2-s2.0-85160038835&origin=inward&txGid=3e6476c827a8e3b942827a756098dd80Decision tree as a screening tool for the diagnosis of the metabolic syndrome without doing a blood testArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/drafthttp://purl.org/coar/version/c_b1a7d7d4d402bcceInternational Journal of Artificial IntelligenceAguilar, M., Bhuket, T., Torres, S., Liu, B., Wong, R. J. Prevalence of the metabolic syndrome in the united states, 2003-2012 (2015) JAMA, 313, p. 1973.Alberti, K., Eckel, R. H., Grundy, S. M., Zimmet, P. Z., Cleeman, J. I., Donato, K. A., Fruchart, J.-C., Smith, S. C. Harmonizing the metabolic syndrome (2009) Circulation, 120, pp. 1640-1645.Alberti, K. G. M. M., Zimmet, P., Shaw, J., George, . K., Alberti, M. M., Aschner, P., Balkau, B., Unwin, N. Metabolic syndrome-a new world-wide definition. a consensus statement from the international diabetes federation (2006),Alberti, K. G. M. M., Zimmet, P Z. (1997) Definition, diagnosis and classification of diabetes mellitus and its complications part 1: Diagnosis and classification of diabetes mellitus provisional report of a who consultation original articles,Aschner, P. Metabolic syndrome as a risk factor for diabetes (2010) Expert Review of Cardiovascular Therapy, 8, pp. 407-412.Asman, S. H., Aziz, N. F. A., Amirulddin, U. A. U., Kadir, M. Z. A. A. Decision tree method for fault causes classification based on rms-dwt analysis in 275 kv transmission lines network (2021) Applied Sciences, 11, p. 4031.Barengo, N. C., Katoh, S., Moltchanov, V., Tajima, N., Tuomilehto, J. The diabetes-cardiovascular risk paradox: Results from a finnish population-based prospective study (2008) European Heart Journal, 29, pp. 1889-1895.Barrios, M., Jimeno, M., Villalba, P, Navarro, E. Novel Data Mining Methodology for Healthcare Applied to a New Model to Diagnose Metabolic Syndrome without a Blood Test (2019) Diagnostics, 9 (4), p. 192.Barrios, M., Jimeno, M., Villalba, P, Navarro, E. Framework to diagnose the metabolic syndrome types without using a blood test based on machine learning (2020) Applied Sciences, 10, p. 8404.(2017) National Diabetes Statistics Report, 2017, p. 2009. Centers for Disease Control and Prevention US Department of Health and Human Services (Cdc): 2012Chen, H., Xiong, S., Ren, X. Evaluating the risk of metabolic syndrome based on an artificial intelligence model (2014) Abstract and Applied Analysis, 2014, pp. 1-12.Chobanian, A. V., Bakris, G. L., Black, H. R., Cushman, W. C., Green, L. A., Izzo, J. L., Jones, D. W., Roccella, E. J. Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure (2003) Hypertension, 42, pp. 1206-1252.Cleophas, T. J., Zwinderman, A. H. (2015) Machine Learning in Medicine - a Complete Overview, Springer International PublishingCornier, M.-A., Dabelea, D., Hernandez, T. L., Lindstrom, R. C., Steig, A. J., Stob, N. R., Pelt, R. E. V., Eckel, R. H. The metabolic syndrome (2008) Endocrine Reviews, 29, pp. 777-822.Datta, S., Schraplau, A., da Cruz, H. F, Sachs, J. P., Mayer, F., Bottinger, E. A machine learning approach for non-invasive diagnosis of metabolic syndrome (2019) IEEE, pp. 933-940.Deshmukh, P., Garg, N., Goswami, K., Bhalavi, V. Prevalence and correlates of metabolic syndrome in the adolescents of rural wardha (2015) Indian Journal of Community Medicine, 40, p. 43.Díaz-Apodaca, B. A., Ebrahim, S., McCormack, V., de Cosío, F. G., Ruiz-Holguín, R. Prevalence of type 2 diabetes and impaired fasting glucose: Cross-sectional study of multiethnic adult population at the united states-mexico border (2010) Revista Panamericana de Salud Publica, 28, pp. 174-181.Esposito, K., Chiodini, P., Colao, A., Lenzi, A., Giugliano, D. Metabolic syndrome and risk of cancer: A systematic review and meta-analysis (2012) Diabetes Care, 35, pp. 2402-2411.Executive summary of the third report of the national cholesterol education program (ncep) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel iii) (2001) JAMA: The Journal of the American Medical Association, 285, pp. 2486-2497.Ford, E. S., Giles, W. H., Dietz, W. H. Prevalence of the metabolic syndrome among us adults (2002) JAMA, 287, p. 356.Grundy, S. M. Metabolic syndrome: Connecting and reconciling cardiovascular and diabetes worlds (2006) Journal of the American College of Cardiology, 47, pp. 1093-1100.Grundy, S. M. 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Monte carlo cross validation (2001) Chemometrics and Intelligent Laboratory Systems, 56, pp. 1-11.131109121Decision TreeDiagnostic non-invasiveMetabolic syndromeRandom subsampling validationScreening ToolPublicationORIGINALDecision Tree as a screening tool for the diagnosis of the Metabolic Syndrome without doing a blood test.pdfDecision Tree as a screening tool for the diagnosis of the Metabolic Syndrome without doing a blood test.pdfapplication/pdf204776https://repositorio.cuc.edu.co/bitstreams/0c6233f2-e5bd-4f93-ac91-fe966fd42a8a/downloade9fc18bbd8e05c3d0a2c51a945d0525cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/6d8e7529-70ae-4a16-8763-11c6381de3c5/download73a5432e0b76442b22b026844140d683MD52TEXTDecision Tree as a screening tool for the diagnosis of the Metabolic Syndrome without doing a blood test.pdf.txtDecision Tree as a screening tool for the diagnosis of the Metabolic Syndrome without doing a blood test.pdf.txtExtracted texttext/plain13225https://repositorio.cuc.edu.co/bitstreams/cc8b0bf8-3755-4d13-8ab6-a1541bab3f0f/downloadfa09375829ac6a69a6f2e5dfe831da09MD53THUMBNAILDecision Tree as a screening tool for the diagnosis of the Metabolic Syndrome without doing a blood test.pdf.jpgDecision Tree as a screening tool for the diagnosis of the Metabolic Syndrome without doing a blood test.pdf.jpgGenerated Thumbnailimage/jpeg15578https://repositorio.cuc.edu.co/bitstreams/c421f4ea-7955-4beb-b1ad-db42d8aef29f/downloada4128d8106078a43eccd3f2d6987d158MD5411323/13650oai:repositorio.cuc.edu.co:11323/136502024-11-09 03:01:17.907https://creativecommons.org/licenses/by-sa/4.0/© 2023, Centre for Environment and Socio-Economic Research Publications. 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CUCrepdigital@cuc.edu.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