Study of artificial intelligence algorithms most used for the diagnosis of type 2 diabetes mellitus
DOI:
https://doi.org/10.54753/cedamaz.v13i1.1804Keywords:
Type 2 diabetes, Artificial intelligence techniques, Diabetes predictionAbstract
Diabetes is the second leading cause of death worldwide, especially in low-income countries. In Ecuador, one in ten people is diagnosed with type 2 diabetes mellitus, this is due to risk factors such as: family history of diabetes, medication, sedentary lifestyle or poor diet. Therefore, it is essential to carry out a Systematic Literature Review on the state of the use of Artificial Intelligence techniques for the diagnosis of type 2 diabetes mellitus; in order to answer the question: What are the artificial intelligence techniques applied to the diagnosis of type 2 diabetes mellitus?Metrics
References
A.L.A.D. (2019). Guías alad sobre el diagnóstico, control y tratamiento de la diabetes mellitus tipo 2 con medicina basada en evidencia edición 2019”. Rev. la Asoc. Latinoam. diabetes, 2019. Descargado de www.revistaalad.com.
Alehegn, M., y Joshi, R. (2019). Type ii diabetes prediction using combo of svm”. Int.J. Eng. Adv. Technol, 8, núm. 6, 712–715,. doi: 10.35940/ijeat.F7974.088619.
Baltar, F., y Gorjup, M. (2014). Muestreo mixto online: Una aplicación en poblaciones ocultas”. Intang. Cap, núm. 1, 2012–2020,. doi: 10.3926/ic.294.
Barbara, K. (2007). Guidelines for performing systematic literature reviews in software engineering”.
Barrios, D., y Infantes, E. (2018). Modelo predictivo para el diagnóstico de la diabetes mellitus tipo 2 soportado por sap predictive analytics”. UNIVERSIDAD PERUANA DE CIENCIAS APLICADAS.
Barrios, O., Alberto, D., Infantes, V., Raphael, E., Aguirre, A., y Alexander, J. (2017a). Predictive modeling for
presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis”. doi: 10.1109/INTERCON.2017.8079667.
Barrios, O., Alberto, D., Infantes, V., Raphael, E., Aguirre, A., y Alexander, J. (2017b). Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis”. en 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON, 1–4,. doi: 10.1109/INTERCON.2017.8079667.
Bello, L. (2016). El riesgo de los que cuidan el riesgo: Findrisk en personal de blanco.”. Rev. virtual Soc. Parag. Med. Int, 3, núm. 2, 71–76,. doi: 10.18004/rvspmi/.
Benítez, R., Escudero, G., y Kanaan, S. (2013). Inteligencia artificial avanzada, editorial. Barcelona.
Bocco, M., Lemus, J., y Velthuis, M. (2014). Métodos de investigación en ingeniería del software”.
Brereton, P., Kitchenham, B., Budgen, D., Turner, M., y Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain”. J. Syst. Softw, 80, núm. 4, 571–583,. doi: 10.1016/j.jss.2006.07.009.
Carrizo, D., y Moller, C. (2018). Estructuras metodológicas de revisiones sistemáticas de literatura en ingeniería de software: un estudio de mapeo sistemático methodological structures of systematic literature review in software engineering: a systematic mapping study”.
Chen, W., Chen, S., Zhang, H., y Wu, T. (2017). A hybrid prediction model for type 2 diabetes using k-means and decision tree”. Proc. IEEE Int. Conf. Softw. Eng. Serv. Sci. ICSESS, núm, 61272399, 386–390,. doi: 10.1109/ICSESS.2017.8342938.
Daanouni, O., Cherradi, B., y Tmiri, A. (2019). Predicting diabetes diseases using mixed data and supervised machine learning algorithms”. doi: 10.1145/3368756.3369072.
Derevitskii, I., y Kovalchuk, S. (2019). Analysis course of the disease of type 2 diabetes patients using markov chains and clustering methods.”. Procedia Comput. Sci, 156, 114–122,. doi: 10.1016/j.procs.2019.08.186.
Dong, Y., Wen, R., Li, Z., Zhang, K., y Zhang, L. (2019). Clu-rnn: A new rnn based approach to diabetic blood glucose prediction”. En en 2019 ieee 7th international conference on bioinformatics and computational biology ( icbcb (p. 50–55,). doi: 10.1109/ICBCB.2019.8854670.
Durgadevi, M., y Kalpana, R. (2017). Performance analysis of classification approaches for the prediction of type ii diabetes”. En en 2017 ninth international conference on advanced computing (icoac (p. 339–344,). doi: 10.1109/ICoAC.2017.8441197.
Freddy, , y Viera, G. (2015). Técnicas de aprendizaje de máquina utilizadas para la minería de texto” (Vol. 31). G, (2005). Revisiones sistemáticas de la literatura”.
Ganesh, P., y Sripriya, P. (2020). A comparative review of prediction methods for pima indians diabetes dataset”. Adv. Intell. Syst. Comput(C), 735–750,. doi: 10.1007/978-3-030-37218-7_83.
INEC. (2017). Instituto nacional de estadísticas y censos, diabetes, segunda causa de muerte después de las enfermedades isquémicas del corazón. INEC. Descargado de https://www .ecuadorencifras .gob.ec/diabetes -segunda -causa -de -muertedespues -de-las -enfermedades -isquemicas -del -corazon /(consultado mar. 15, 2020).)
International Diabetes Federation, A., y Séptima. (2015). Kadhm, M., Ghindawi, I., Of, D.-I., y U. (2018). An accurate diabetes prediction system based on k-means clustering and proposed classification approach”. ripublication. com, 13, 4038–4041,.
Kazerouni, F., Bayani, A., Asadi, F., Saeidi, L., Parvizi, N., y Mansoori, Z. (2020). Type2 diabetes mellitus prediction using data mining algorithms based on the longnoncoding rnas expression: A comparison of four data mining approaches”. BMC Bioinformatics, 21, núm. 1. doi: 10.1186/s12859-020-03719-8.
Kitchenham, B. (2004). Procedures for performing systematic reviews”. Br. J. Manag, 14, núm. 0, 207–222,. doi:10.1111/1467-8551.00375.
Kopitar, L., Cilar, L., Kocbek, P., y Stiglic, G. (2019). Local vs. global interpretability of machine learning models in type 2 diabetes mellitus screening”. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics, 11979 LNAI, 108–119,. doi:10.1007/978-3-03037446-4_9.
Kumari, M., Vohra, R., y Arora, A. (2014). Prediction of diabetes using bayesian network”. Int. J. Comput. Sci. Inf. Technol, 5, núm. 4, 5174–5178,. Descargado de http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.640.3573.
Kumari, V., Chitra, R., Castrillón, O., Sarache,W., y Castaño, E. (2013). Classification of diabetes disease using support vector machine”. Int. J. Eng. Res. Appl, 3, núm. 2, 1797–1801,. doi: 10.4067/S0718-07642017000600017.
Lantz, B. (2019). Machine learning with r (Vol. 3). Packt Publishing.
Libed, J., Perreras, R., y Carpio, J. (2020). Type ii diabetes analysis using naïve bayesian classification algorithm”. ACM Int. Conf. Proceeding Ser, 35–39,. doi: 10.1145/3424311.3424327.
Loku, L., Fetaji, B., y Fetaji, M. (2020). Prevention of diabetes by devising a prediction analytics model”. doi:10.1109/HORA49412.2020.9152894.
Lukmanto, R., Suharjito, A., y Akbar, H. (2019). Early detection of diabetes mellitus using feature selection and fuzzy support vector machine”. Procedia Comput. Sci, 157, 46–54,. Descargado de https://doi.org/10.1016/j.procs.2019.08.140. doi: 10 .1016/j.procs.2019.08.140.
Mosquera, R., Castrillón, O., y Parra, L. (2018). Máquinas de soporte vectorial, clasificador naïve bayes y algoritmos genéticos para la predicción de riesgos psicosociales en docentes de colegios públicos colombianos”. Inf. tecnológica, 29, núm. 6, 153–162,. doi:
4067/s0718-07642018000600153.
Organizacion mundial de la salud, informe mundial sobre la diabetes. (2016).
P, B., R, S., K, N., y K, A. (2020). Type 2: Diabetes mellitus prediction using deep neural networks classifier”. Int. J. Cogn. Comput. Eng, 1, 55–61,. Descargado de https://doi.org/10.1016/j.ijcce.2020.10.002. doi: 10.1016/j.ijcce.2020.10.002.
Patil, R., Tamane, S., y Patil, K. (2020). Self organising fuzzy logic classifier for predicting type-2 diabetes mellitus using aco-ann”. Int. J. Adv. Comput. Sci. Appl, 11, núm. 7, 348–353,. doi: 10.14569/IJACSA.2020.0110746.
Petticrew, M., y Roberts, H. (2008). Systematic reviews in the social sciences: A practical guide.
Ram, G., y Christian, S. (2016). Agrupamiento de textos cortos en dominios cruzados cross-domain clustering for short texts” (Vol. 115).
Rodríguez, F. (2017). Métodos de clasificación | estadística y machine learning con r.”. en Estadística y Machine Learning con R.
Sampieri, R., Collado, C., y Pilar Baptista Lucio, M. (1997). Metodología de la investigación (5ta Ed” ed.).
Sataloff, R., Johns, M., y Kost, K. (2015). Obesidad, diabetes mellitus y sindrome metabólico”. En en harrison principios de medicina interna, novena (Vol. 2, p. 2392–2449).
Shetty, G., y Katkar, V. (2019a). Type-ii diabetes detection using decision-tree based ensemble of classifiers”. doi:10.1109/ICCUBEA47591.2019.9129348.
Shetty, G., y Katkar, V. (2019b). Type-ii diabetes detection using decision-tree based ensemble of classifiers”. En en 2019 5th international conference on computing,communication, control and automation (iccubea(p. 1–5,). doi: 10.1109/ICCUBEA47591.2019.9129348.
Singh, N., y Singh, P. (2020). A stacked generalization approach for diagnosis and prediction of type 2 diabetes
mellitus”. Adv. Intell. Syst. Comput, 990, 559–570,. doi: 10.1007/978-981-13-8676-3_47.
S. OMS, O. (2020). Diabetes”. Descargado de https://www .who .int/es/news -room/fact -sheets/detail/diabetes
Toro, J., Giraldo, S., y Plaza, J. (2020). Utilización de algoritmos de clasificación bayesiana y sistemas inteligentes para el desarrollo de un prototipo software para el diagnóstico de diabetes mellitus tipo 2”. en Investigación Formativa en Ingeniería, Cuarta Edi., Medellyn, 295.
Verónica, N., Pérez, R., Estrada, M., Miriam, A., y Tovar, D. (2015). Aplicación métodos de inteligencia artificial en el área médica.”. Pist. Educ. Inst. Tecnológico Celaya., núm, 111, 124–130,.
Vidhya, K., y Shanmugalakshmi, R. (2020a). Deep learning based big medical data analytic model for diabetes complication prediction”. J. Ambient Intell. Humaniz. Comput, 11, núm. 11, 5691–5702,. doi: 10.1007/s12652-020-01930-2.
Vidhya, K., y Shanmugalakshmi, R. (2020b). Deep learning based big medical data analytic model for diabetes complication prediction”. J. Ambient Intell. Humaniz. Comput, 11, núm. 11, 5691–5702,. doi: 10.1007/s12652-020-01930-2.
Wang, C. (2013). Evaluating the risk of type 2 diabetes mellitus using artificial neural network: An effective classification approach”. Diabetes Res. Clin. Pract, 100, núm. 1, 111–118,. doi: 10.1016/j.diabres.2013.01.023.
Wu, H., Yang, S., Huang, Z., He, J., yWang, X. (2018). Type 2 diabetes mellitus prediction model based on data mining”. Informatics Med. Unlocked, 10, 100–107,. doi: 10.1016/j.imu.2017.12.006.
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 CEDAMAZ
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Those authors who have publications with this journal, accept the following terms:
- After the scientific article is accepted for publication, the author agrees to transfer the rights of the first publication to the CEDAMAZ Journal, but the authors retain the copyright. The total or partial reproduction of the published texts is allowed as long as it is not for profit. When the total or partial reproduction of scientific articles accepted and published in the CEDAMAZ Journal is carried out, the complete source and the electronic address of the publication must be cited.
- Scientific articles accepted and published in the CEDAMAZ journal may be deposited by the authors in their entirety in any repository without commercial purposes.
- Authors should not distribute accepted scientific articles that have not yet been officially published by CEDAMAZ. Failure to comply with this rule will result in the rejection of the scientific article.
- The publication of your work will be simultaneously subject to the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
