Comparison of bioinspired algorithms applied to cancer database
Cancer is not just a disease; it is a set of diseases. Breast cancer is the second most common cancer worldwide after lung cancer, and it represents the most frequent cause of cancer death in women (Thurtle et al. in: PLoS Med 16(3):e1002758, 2019, 1]). If it is diagnosed at an early age, the chance...
- Autores:
-
Silva, Jesús
Villareal-González, Reynaldo
Varela, Noel
Maco, José
Villón, Martín
Marín–González, Freddy
Pineda Lezama, Omar Bonerge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7700
- Acceso en línea:
- https://hdl.handle.net/11323/7700
https://doi.org/10.1007/978-981-15-7234-0_87
https://repositorio.cuc.edu.co/
- Palabra clave:
- Big data
Machine learning
Cancer prediction
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Comparison of bioinspired algorithms applied to cancer database |
| title |
Comparison of bioinspired algorithms applied to cancer database |
| spellingShingle |
Comparison of bioinspired algorithms applied to cancer database Big data Machine learning Cancer prediction |
| title_short |
Comparison of bioinspired algorithms applied to cancer database |
| title_full |
Comparison of bioinspired algorithms applied to cancer database |
| title_fullStr |
Comparison of bioinspired algorithms applied to cancer database |
| title_full_unstemmed |
Comparison of bioinspired algorithms applied to cancer database |
| title_sort |
Comparison of bioinspired algorithms applied to cancer database |
| dc.creator.fl_str_mv |
Silva, Jesús Villareal-González, Reynaldo Varela, Noel Maco, José Villón, Martín Marín–González, Freddy Pineda Lezama, Omar Bonerge |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Villareal-González, Reynaldo Varela, Noel Maco, José Villón, Martín Marín–González, Freddy Pineda Lezama, Omar Bonerge |
| dc.subject.spa.fl_str_mv |
Big data Machine learning Cancer prediction |
| topic |
Big data Machine learning Cancer prediction |
| description |
Cancer is not just a disease; it is a set of diseases. Breast cancer is the second most common cancer worldwide after lung cancer, and it represents the most frequent cause of cancer death in women (Thurtle et al. in: PLoS Med 16(3):e1002758, 2019, 1]). If it is diagnosed at an early age, the chances of survival are greater. The objective of this research is to compare the performance of method predictions: (i) Logistic Regression, (ii) K-Nearest Neighbor, (iii) K-means, (iv) Random Forest, (v) Support Vector Machine, (vi) Linear Discriminant Analysis, (vii) Gaussian Naive Bayes, and (viii) Multilayer Perceptron within a cancer database. |
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2021 |
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2021-01-15T18:14:09Z |
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2021-01-15T18:14:09Z |
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2021 |
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Artículo de revista |
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1. Thurtle DR, Greenberg DC, Lee LS, Huang HH, Pharoah PD, Gnanapragasam VJ (2019) Individual prognosis at diagnosis in nonmetastatic prostate cancer: development and external validation of the PREDICT Prostate multivariable model. PLoS Med 16(3):e1002758. https://doi.org/10.1371/journal.pmed.1002758 2. Nima T, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312 3. Desautels T, Das R, Calvert J, Trivedi M, Summers C, Wales DJ et al (2017) Prediction of early unplanned intensive care unit read-mission in a UK tertiary care hospital: a cross-sectional machine learning approach. BMJ Open 7:e017199 4. Hahsler M, Karpienko R (2017) Visualizing association rules in hieralchical groups. J Bus Econ 87:317–335 5. Velikova M, Lucas PJF, Samulski M, Karssemeijer N (2013) On the interplay of machine learning and background knowledge in image interpretation by Bayesian networks. Artif Intell Med 57(1):73–86. https://doi.org/10.1016/J.ARTMED.2012.12.004 6. Statnikov A, Wang L, Aliferis CF (2008) A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinform 9:1–10. https://doi.org/10.1186/1471-2105-9-319 7. Olivera AR, Roesler V, Iochpe C, Schmidt MI, Vigo Á, Barreto SM, Duncan BB (2017) Comparisonof machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study. Sao Paulo Med J 135(3):234–246. https://doi.org/10.1590/1516-3180.2016.0309010217 8. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Proc Comput Sci 151:1201–1206 9. Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data, June 21018. Springer, Cham, pp 3–11 10. Chen T, Chefd’hotel C (2014) Deep learning based automatic immune cell detection for immunohistochemistry images. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 17–24 11. Viloria, Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System. Procedia Computer Science, 2019, vol. 155, p. 575–580 12. Clougherty E, Clougherty J, Liu X, Brown D (2015) Spatial and temporal analysis of sex crimes in Charlottesville, Virginia. In: Proceedings of IEEE systems and information engineering design symposium. IEEE, pp 69–74 13. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436 14. Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Asoc 97(457):77–86. https://doi.org/10.1198/016214502753479248 15. D’Amico AC, Renshaw AA, Cote K, Hurwitz M, Beard C, Loffredo M et al (2004) Impact of the percentage of positive prostate cores on prostate cancer-specific mortality for patients with low or favorable intermediate-risk disease. J Clin Oncol 22(18):3726–3732 (pmid: 15365069) 16. Ontario HQ (2017) Prolaris cell cycle progression test for localized prostate cancer: a health technology assessment. Ont Health Technol Assess Ser 17(6):1–75 (pmid: 28572867) 17. Klemann N, Roder MA, Helgstrand JT, Brasso K, Toft BG, Vainer B et al (2017) Risk of prostate cancer diagnosis and mortality in men with a benign initial transrectal ultrasound-guided biopsy set: a population-based study. Lancet Oncol 18(2):221–229 (pmid: 28094199) 18. Turner EL, Metcalfe C, Donovan JL, Noble S, Sterne JA, Lane JA et al (2016) Contemporary accuracy of death certificates for coding prostate cancer as a cause of death: is reliance on death certification good enough? A comparison with blinded review by an independent cause of death evaluation committee. Br J Cancer 115(1):90–94 (pmid: 27253172) 19. Celi LA, Mark RG, Stone DJ, Montgomery RA (2013) “Big Data” in the intensive care unit. Closing the data loop. Am J Respir Crit Care Med 187:1157–1160 20. Andrea DM, Marco G, Michele G (2016) A formal definition of Big Data based on its essential features. Libr Rev 65:122–135 21. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2008) Detecting influenza epidemics using search engine query data. Nature 457:1012 22. Feng M, McSparron JI, Kien DT, Stone DJ, Roberts DH, Schwartzstein RM et al (2018) Transthoracic echocardiography and mortality in sepsis: analysis of the MIMIC-III database. Intensive Care Med 44:884–892 23. Liu WY, Lin SG, Zhu GQ, Poucke SV, Braddock M, Zhang Z et al (2016) Establishment and validation of GV-SAPS II scoring system for non-diabetic critically ill patients. PLoS ONE 11:e0166085 24. Calvert J, Mao Q, Hoffman JL, Jay M, Desautels T, Mohamadlou H et al (2016) Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond) 11:52–57 25. Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L et al (2016) Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Med Inform 4:e28 26. Sandfort V, Johnson AEW, Kunz LM, Vargas JD, Rosing DR (2018) Prolonged elevated heart rate and 90-day survival in acutely ill patients: data from the MIMIC-III database. J Intensive Care Med. https://doi.org/10.1177/0885066618756828 885066618756828 |
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Silva, Jesúse17281d02925301aa71681ad0d7b3e03Villareal-González, Reynaldobb403d59a640e996347bc49afd39cfd6Varela, Noel544417e3ea23421c46114ee4f01f436aMaco, José3ae5a9f0859c736faaf0c56a1aaec025Villón, Martínf63f5e5ca7d5f396ac1e41161469c7c0Marín–González, Freddy59c7f343147de2c248141f4be8c20109Pineda Lezama, Omar Bonergee72941c91bdbbe143e36775e15fb92bd2021-01-15T18:14:09Z2021-01-15T18:14:09Z2021https://hdl.handle.net/11323/7700https://doi.org/10.1007/978-981-15-7234-0_87Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Cancer is not just a disease; it is a set of diseases. Breast cancer is the second most common cancer worldwide after lung cancer, and it represents the most frequent cause of cancer death in women (Thurtle et al. in: PLoS Med 16(3):e1002758, 2019, 1]). If it is diagnosed at an early age, the chances of survival are greater. The objective of this research is to compare the performance of method predictions: (i) Logistic Regression, (ii) K-Nearest Neighbor, (iii) K-means, (iv) Random Forest, (v) Support Vector Machine, (vi) Linear Discriminant Analysis, (vii) Gaussian Naive Bayes, and (viii) Multilayer Perceptron within a cancer database.application/pdfspaCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_87Big dataMachine learningCancer predictionComparison of bioinspired algorithms applied to cancer databaseArtículo 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. Thurtle DR, Greenberg DC, Lee LS, Huang HH, Pharoah PD, Gnanapragasam VJ (2019) Individual prognosis at diagnosis in nonmetastatic prostate cancer: development and external validation of the PREDICT Prostate multivariable model. PLoS Med 16(3):e1002758. https://doi.org/10.1371/journal.pmed.10027582. Nima T, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–13123. Desautels T, Das R, Calvert J, Trivedi M, Summers C, Wales DJ et al (2017) Prediction of early unplanned intensive care unit read-mission in a UK tertiary care hospital: a cross-sectional machine learning approach. BMJ Open 7:e0171994. Hahsler M, Karpienko R (2017) Visualizing association rules in hieralchical groups. J Bus Econ 87:317–3355. Velikova M, Lucas PJF, Samulski M, Karssemeijer N (2013) On the interplay of machine learning and background knowledge in image interpretation by Bayesian networks. Artif Intell Med 57(1):73–86. https://doi.org/10.1016/J.ARTMED.2012.12.0046. Statnikov A, Wang L, Aliferis CF (2008) A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinform 9:1–10. https://doi.org/10.1186/1471-2105-9-3197. Olivera AR, Roesler V, Iochpe C, Schmidt MI, Vigo Á, Barreto SM, Duncan BB (2017) Comparisonof machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study. Sao Paulo Med J 135(3):234–246. https://doi.org/10.1590/1516-3180.2016.03090102178. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Proc Comput Sci 151:1201–12069. Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data, June 21018. Springer, Cham, pp 3–1110. Chen T, Chefd’hotel C (2014) Deep learning based automatic immune cell detection for immunohistochemistry images. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 17–2411. Viloria, Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System. Procedia Computer Science, 2019, vol. 155, p. 575–58012. Clougherty E, Clougherty J, Liu X, Brown D (2015) Spatial and temporal analysis of sex crimes in Charlottesville, Virginia. In: Proceedings of IEEE systems and information engineering design symposium. IEEE, pp 69–7413. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):43614. Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Asoc 97(457):77–86. https://doi.org/10.1198/01621450275347924815. D’Amico AC, Renshaw AA, Cote K, Hurwitz M, Beard C, Loffredo M et al (2004) Impact of the percentage of positive prostate cores on prostate cancer-specific mortality for patients with low or favorable intermediate-risk disease. J Clin Oncol 22(18):3726–3732 (pmid: 15365069)16. Ontario HQ (2017) Prolaris cell cycle progression test for localized prostate cancer: a health technology assessment. Ont Health Technol Assess Ser 17(6):1–75 (pmid: 28572867)17. Klemann N, Roder MA, Helgstrand JT, Brasso K, Toft BG, Vainer B et al (2017) Risk of prostate cancer diagnosis and mortality in men with a benign initial transrectal ultrasound-guided biopsy set: a population-based study. Lancet Oncol 18(2):221–229 (pmid: 28094199)18. Turner EL, Metcalfe C, Donovan JL, Noble S, Sterne JA, Lane JA et al (2016) Contemporary accuracy of death certificates for coding prostate cancer as a cause of death: is reliance on death certification good enough? A comparison with blinded review by an independent cause of death evaluation committee. Br J Cancer 115(1):90–94 (pmid: 27253172)19. Celi LA, Mark RG, Stone DJ, Montgomery RA (2013) “Big Data” in the intensive care unit. Closing the data loop. Am J Respir Crit Care Med 187:1157–116020. Andrea DM, Marco G, Michele G (2016) A formal definition of Big Data based on its essential features. Libr Rev 65:122–13521. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2008) Detecting influenza epidemics using search engine query data. Nature 457:101222. Feng M, McSparron JI, Kien DT, Stone DJ, Roberts DH, Schwartzstein RM et al (2018) Transthoracic echocardiography and mortality in sepsis: analysis of the MIMIC-III database. Intensive Care Med 44:884–89223. Liu WY, Lin SG, Zhu GQ, Poucke SV, Braddock M, Zhang Z et al (2016) Establishment and validation of GV-SAPS II scoring system for non-diabetic critically ill patients. PLoS ONE 11:e016608524. Calvert J, Mao Q, Hoffman JL, Jay M, Desautels T, Mohamadlou H et al (2016) Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond) 11:52–5725. Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L et al (2016) Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Med Inform 4:e2826. Sandfort V, Johnson AEW, Kunz LM, Vargas JD, Rosing DR (2018) Prolonged elevated heart rate and 90-day survival in acutely ill patients: data from the MIMIC-III database. 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