Diabetes diagnostic prediction using vector support machines
The most important factors for the diagnosis of diabetes mellitus (DM) are age, body mass index (BMI) and blood glucose concentration. Diagnosis of DM by a doctor is complicated, because several factors are involved in the disease, and the diagnosis is subject to human error. A blood test does not p...
- Autores:
-
amelec, viloria
Herazo-Beltrán, Yaneth
Cabrera, Danelys
Bonerge Pineda, Omar
- 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:11323/6460
- Acceso en línea:
- https://hdl.handle.net/11323/6460
https://repositorio.cuc.edu.co/
- Palabra clave:
- Medical diagnosis
Diabetes mellitus
Medical computing
Machine learning
Vector support machines
- Rights
- openAccess
- License
- CC0 1.0 Universal
| id |
RCUC2_c2c8ca720894f7c44ccd737fb5036cfd |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/6460 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Diabetes diagnostic prediction using vector support machines |
| title |
Diabetes diagnostic prediction using vector support machines |
| spellingShingle |
Diabetes diagnostic prediction using vector support machines Medical diagnosis Diabetes mellitus Medical computing Machine learning Vector support machines |
| title_short |
Diabetes diagnostic prediction using vector support machines |
| title_full |
Diabetes diagnostic prediction using vector support machines |
| title_fullStr |
Diabetes diagnostic prediction using vector support machines |
| title_full_unstemmed |
Diabetes diagnostic prediction using vector support machines |
| title_sort |
Diabetes diagnostic prediction using vector support machines |
| dc.creator.fl_str_mv |
amelec, viloria Herazo-Beltrán, Yaneth Cabrera, Danelys Bonerge Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Herazo-Beltrán, Yaneth Cabrera, Danelys Bonerge Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Medical diagnosis Diabetes mellitus Medical computing Machine learning Vector support machines |
| topic |
Medical diagnosis Diabetes mellitus Medical computing Machine learning Vector support machines |
| description |
The most important factors for the diagnosis of diabetes mellitus (DM) are age, body mass index (BMI) and blood glucose concentration. Diagnosis of DM by a doctor is complicated, because several factors are involved in the disease, and the diagnosis is subject to human error. A blood test does not provide enough information to make a correct diagnosis of the disease. A vector support machine (SVM) was implemented to predict the diagnosis of DM based on the factors mentioned in patients. The classes of the output variable are three: without diabetes, with a predisposition to diabetes and with diabetes. An SVM was obtained with an accuracy of 99.2% with Colombian patients and an accuracy of 65.6% with a data set of patients of a different ethnic background. |
| publishDate |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2020-07-05T18:42:18Z |
| dc.date.available.none.fl_str_mv |
2020-07-05T18:42:18Z |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
acceptedVersion |
| dc.identifier.issn.spa.fl_str_mv |
1877-0509 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/6460 |
| dc.identifier.doi.spa.fl_str_mv |
10.1016/j.procs.2020.03.065 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
1877-0509 10.1016/j.procs.2020.03.065 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/6460 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
[1] Bates, D., Mäechler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models using lme4. Journal of Statistical Software, 67(1), 1-48. doi: 10.18637/jss.v067.i01. [2] INEGI, “Estadistica a Proporsito del Día Mundial de la Diabetes,” Día Mund. la Diabetes., p. 18, 2013. [3] T. Santhanam and M. S. Padmavathi, “Application of K-Means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis,” Procedia Comput. Sci., vol. 47, no. C, pp. 76–83, 2014. [4] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [5] S. Li, H. Zhao, Z. Ru, and Q. Sun, “Probabilistic back analysis based on Bayesian and multi-output support vector machine for a high cut rock slope,” Eng. Geol., vol. 203, pp. 178–190, 2016. [6] T. Zheng et al., “A machine learning-based framework to identify type 2 diabetes through electronic health records,” Int. J. Med. Inform., vol. 97, pp. 120–127, 2017. [7]Shankaracharya, D. Odedra, S. Samanta, and A. S. Vidyarthi, “Computational intelligence in early diabetes diagnosis: A review,” Rev. Diabet. Stud., vol. 7, no. 4, pp. 252–261, 2010. [8] K. V. S. R. P. Varma, A. A. Rao, T. Sita Maha Lakshmi, and P. V. Nageswara Rao, “A computational intelligence approach for a better diagnosis of diabetic patients,” Comput. Electr. Eng., vol. 40, no. 5, pp. 1758–1765, 2014. [9] D. Çalişir and E. Dogantekin, “An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier,” Expert Syst. Appl., vol. 38, no. 7, pp. 8311–8315, 2011. [10] H. Temurtas, N. Yumusak, and F. Temurtas, “A comparative study on diabetes disease diagnosis using neural networks,” Expert Syst. Appl., vol. 36, no. 4, pp. 8610–8615, 2009. [11] Mellado A., Suárez, N., Altimir, C., Martínez, C., Pérez J. C., Krause, M., & Horvath, A. (2017) Disentangling the change-alliance relationship: Observational assessment of the therapeutic alliance during change and stuck episodes. Psychotherapy Research, 27(5), 595-607. doi: 10.1080/10503307.2016.1147657 [12] Ogles, B. M. (2013). Measuring change in psychotherapy research. En M. J. Lambert (Ed.), Bergin and Garfields’s Handbook of Psychotherapy and Behavior Change (pp.134– 166). New Jersey: Wiley. [13] El Pasante, «Ventajas y desventajas de las bases de datos,» 17 Junio 2015. [En línea]. Available: https://educacion.elpensante.com/ventajasy-desventajas-de-las-bases-de- datos/. [Último acceso: 12 Noviembre 2018]. [14] Probability Formula, «Hypergeometric Distribution,» [En línea]. Available: http://www.probabilityformula.org/hypergeometricdistribution.html. [Último acceso: 16 Noviembre 2018]. [15] Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling. Boca Raton: Chapman & Hall/CRC [16] J. Swamidass† y P. Baldi, «Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval,» Journal of Chemical Information and Modeling, vol. 47, nº 1, pp. 952-964, 2006. [17] S. Arif, J. Holliday y P. Willett, «Comparison of chemical similarity measures using different numbers of query structures,» Journal of Information Science, vol. 39, nº 1, pp. 1-8, 2013. [18] Bucci, N., Luna, M., Viloria, A., García, J. H., Parody, A., Varela, N., & López, L. A. B. [2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data [pp. 149-158). Springer, Cham [19] Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J. P., Parody, A., Bent, D. E. S., & López, L. A. B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham. |
| dc.rights.spa.fl_str_mv |
CC0 1.0 Universal |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.publisher.spa.fl_str_mv |
Procedia Computer Science |
| institution |
Corporación Universidad de la Costa |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/4e42ddd6-cf3d-460d-8a23-ead7d7466e4a/download https://repositorio.cuc.edu.co/bitstreams/679f576d-b933-49c4-995a-3f8c633fc289/download https://repositorio.cuc.edu.co/bitstreams/f66bd18f-9a72-4da7-9a41-77ac66a47f95/download https://repositorio.cuc.edu.co/bitstreams/13e1d2ee-1382-4728-b589-66bd791ca95c/download https://repositorio.cuc.edu.co/bitstreams/4d947420-cf31-4269-8711-5a01ec8f0004/download |
| bitstream.checksum.fl_str_mv |
18012e6a5cab67c03e0031bef3443c51 42fd4ad1e89814f5e4a476b409eb708c e30e9215131d99561d40d6b0abbe9bad d28367678808007eb7c8955b185d4c92 722052201bb70c4625675655524ebdb1 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1811760845399523328 |
| spelling |
amelec, viloriaHerazo-Beltrán, YanethCabrera, DanelysBonerge Pineda, Omar2020-07-05T18:42:18Z2020-07-05T18:42:18Z20201877-0509https://hdl.handle.net/11323/646010.1016/j.procs.2020.03.065Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The most important factors for the diagnosis of diabetes mellitus (DM) are age, body mass index (BMI) and blood glucose concentration. Diagnosis of DM by a doctor is complicated, because several factors are involved in the disease, and the diagnosis is subject to human error. A blood test does not provide enough information to make a correct diagnosis of the disease. A vector support machine (SVM) was implemented to predict the diagnosis of DM based on the factors mentioned in patients. The classes of the output variable are three: without diabetes, with a predisposition to diabetes and with diabetes. An SVM was obtained with an accuracy of 99.2% with Colombian patients and an accuracy of 65.6% with a data set of patients of a different ethnic background.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Herazo-Beltrán, Yaneth-will be generated-orcid-0000-0003-3752-4353-600Cabrera, Danelys-will be generated-orcid-0000-0002-9486-9764-600Bonerge Pineda, OmarengProcedia Computer ScienceCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Medical diagnosisDiabetes mellitusMedical computingMachine learningVector support machinesDiabetes diagnostic prediction using vector support machinesArtículo 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/acceptedVersion[1] Bates, D., Mäechler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models using lme4. Journal of Statistical Software, 67(1), 1-48. doi: 10.18637/jss.v067.i01.[2] INEGI, “Estadistica a Proporsito del Día Mundial de la Diabetes,” Día Mund. la Diabetes., p. 18, 2013.[3] T. Santhanam and M. S. Padmavathi, “Application of K-Means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis,” Procedia Comput. Sci., vol. 47, no. C, pp. 76–83, 2014.[4] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham[5] S. Li, H. Zhao, Z. Ru, and Q. Sun, “Probabilistic back analysis based on Bayesian and multi-output support vector machine for a high cut rock slope,” Eng. Geol., vol. 203, pp. 178–190, 2016.[6] T. Zheng et al., “A machine learning-based framework to identify type 2 diabetes through electronic health records,” Int. J. Med. Inform., vol. 97, pp. 120–127, 2017.[7]Shankaracharya, D. Odedra, S. Samanta, and A. S. Vidyarthi, “Computational intelligence in early diabetes diagnosis: A review,” Rev. Diabet. Stud., vol. 7, no. 4, pp. 252–261, 2010.[8] K. V. S. R. P. Varma, A. A. Rao, T. Sita Maha Lakshmi, and P. V. Nageswara Rao, “A computational intelligence approach for a better diagnosis of diabetic patients,” Comput. Electr. Eng., vol. 40, no. 5, pp. 1758–1765, 2014.[9] D. Çalişir and E. Dogantekin, “An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier,” Expert Syst. Appl., vol. 38, no. 7, pp. 8311–8315, 2011.[10] H. Temurtas, N. Yumusak, and F. Temurtas, “A comparative study on diabetes disease diagnosis using neural networks,” Expert Syst. Appl., vol. 36, no. 4, pp. 8610–8615, 2009.[11] Mellado A., Suárez, N., Altimir, C., Martínez, C., Pérez J. C., Krause, M., & Horvath, A. (2017) Disentangling the change-alliance relationship: Observational assessment of the therapeutic alliance during change and stuck episodes. Psychotherapy Research, 27(5), 595-607. doi: 10.1080/10503307.2016.1147657[12] Ogles, B. M. (2013). Measuring change in psychotherapy research. En M. J. Lambert (Ed.), Bergin and Garfields’s Handbook of Psychotherapy and Behavior Change (pp.134– 166). New Jersey: Wiley.[13] El Pasante, «Ventajas y desventajas de las bases de datos,» 17 Junio 2015. [En línea]. Available: https://educacion.elpensante.com/ventajasy-desventajas-de-las-bases-de- datos/. [Último acceso: 12 Noviembre 2018].[14] Probability Formula, «Hypergeometric Distribution,» [En línea]. Available: http://www.probabilityformula.org/hypergeometricdistribution.html. [Último acceso: 16 Noviembre 2018].[15] Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling. Boca Raton: Chapman & Hall/CRC[16] J. Swamidass† y P. Baldi, «Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval,» Journal of Chemical Information and Modeling, vol. 47, nº 1, pp. 952-964, 2006.[17] S. Arif, J. Holliday y P. Willett, «Comparison of chemical similarity measures using different numbers of query structures,» Journal of Information Science, vol. 39, nº 1, pp. 1-8, 2013.[18] Bucci, N., Luna, M., Viloria, A., García, J. H., Parody, A., Varela, N., & López, L. A. B. [2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data [pp. 149-158). Springer, Cham[19] Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J. P., Parody, A., Bent, D. E. S., & López, L. A. B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham.PublicationORIGINALDiabetes diagnostic prediction using vector support machines.pdfDiabetes diagnostic prediction using vector support machines.pdfapplication/pdf927921https://repositorio.cuc.edu.co/bitstreams/4e42ddd6-cf3d-460d-8a23-ead7d7466e4a/download18012e6a5cab67c03e0031bef3443c51MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/679f576d-b933-49c4-995a-3f8c633fc289/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/f66bd18f-9a72-4da7-9a41-77ac66a47f95/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILDiabetes diagnostic prediction using vector support machines.pdf.jpgDiabetes diagnostic prediction using vector support machines.pdf.jpgimage/jpeg44553https://repositorio.cuc.edu.co/bitstreams/13e1d2ee-1382-4728-b589-66bd791ca95c/downloadd28367678808007eb7c8955b185d4c92MD54TEXTDiabetes diagnostic prediction using vector support machines.pdf.txtDiabetes diagnostic prediction using vector support machines.pdf.txttext/plain35683https://repositorio.cuc.edu.co/bitstreams/4d947420-cf31-4269-8711-5a01ec8f0004/download722052201bb70c4625675655524ebdb1MD5511323/6460oai:repositorio.cuc.edu.co:11323/64602024-09-17 14:09:54.123http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |