DOI : https://doi.org/10.18517/ijaseit.12.3.11438

A Predictive Model for an Assistant System to identify Diabetes Mellitus

Elena Fabiola Ruiz-Ledesma (1), Laura Ivoone Garay-Jiménez (2), Elizabeth Acosta-Gonzaga (3)
(1) Instituto Politécnico Nacional, Escuela Superior de Cómputo, Col. La Escalera, Mexico City, 07320, Mexico
(2) Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Mexico City, 07340, Mexico
(3) Instituto Politécnico Nacional, Unidad Interdisciplinaria de Ingeniería Ciencias Sociales y Administrativas, Mexico City, 08400, Mexico
Fulltext View | Download
How to cite (IJASEIT) :
Ruiz-Ledesma, Elena Fabiola, et al. “A Predictive Model for an Assistant System to Identify Diabetes Mellitus”. International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 3, June 2022, pp. 1008-17, doi:10.18517/ijaseit.12.3.11438.
Citation Format :
Given the high incidence in Mexico of Diabetes Mellitus (DM), it seems impossible for all patients to be monitored by a specialist. Therefore, preventive action and primary care contact services are an urgent need for early detection of this disease. This paper aims at proposing a model to calculate the risk factor from the information provided by the participant without requiring clinical studies. Firstly, the principal causes of DM are identified via an initial state-of-art analysis about DM pre-screening, screening, diagnosis, and treatment. Then, a digital survey was applied to a sample of fifty participants, all of whom were asked about their knowledge of DM’s causes. Afterward, an in-depth qualitative analysis of the answers was made. Results showed that only 36% of the interviewed subjects identified the main symptoms of DM, but 66% would be willing to consult with a doctor if they could identify the symptoms. The proposed digital system measures the perception of the user and provides quick information about the disease, considering DM Type 1 statistics. The system is based on conditional probability and clinic guides for prevention by identifying risk factors and pondering. Further extensive use of this assistance could raise awareness about the risk factor and provided a percentage of the probability of having DM Type 1, and the result could help detect DM in the early stages.

A. M. Egan and S. F. Dinneen, “What is diabetes?,” Medicine (Baltimore)., vol. 47, no. 1, pp. 1-4, 2019.

A. D. Association, “2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2018,” Diabetes Care, vol. 41, no. Supplement 1, pp. S13-S27, 2018.

L. P. Nandhini, S. Kamalanathan, and J. Sahoo, “Definition, Diagnostic Criteria, Screening, Diagnosis, and Classification of Diabetes and Categories of Glucose Intolerance,” in The Diabetes Textbook, Springer, 2019, pp. 71-85.

E. W. Gregg and P. Bracco, “The Dynamics of Diabetes Prevalence, Morbidity, and Mortality,” in The Diabetes Textbook, Springer, 2019, pp. 11-21.

J.-M. Ekoe, R. Goldenberg, and P. Katz, “Screening for diabetes in adults,” Can. J. diabetes, vol. 42, pp. S16-S19, 2018.

L. E. Moore, “Screening, Diagnosis, and Management of Gestational Diabetes,” in Diabetes in Pregnancy, Springer, 2018, pp. 45-59.

R. Rojas-Martí­nez, A. Basto-Abreu, C. A. Aguilar-Salinas, E. Zí¡rate-Rojas, S. Villalpando, and T. Barrientos-Gutií©rrez, “Prevalencia de diabetes por diagnóstico mí©dico previo en Mí©xico,” Salud Publica Mex., vol. 60, no. 3, pp. 224-232, 2018.

C. B. Williams, D. Y. LaCoursiere, G. A. Talavera, and S. Gahagan, “A feasibility study to promote optimal weight in first time pregnant mothers and their babies: lessons learned in a US-Mexico border community,” Matern. Child Health J., vol. 23, no. 5, pp. 578-584, 2019.

T. C. Aburto, J. M. Poti, and B. M. Popkin, “Patterns and trends in the intake distribution of manufactured and homemade sugar-sweetened beverages in pre-tax Mexico, 1999-2012,” Public Health Nutr., vol. 21, no. 18, pp. 3296-3306, 2018.

A. Arredondo, E. Orozco, M. B. Duarte, S. M. Cuadra, A. L. Recamí¡n, and A. Azar, “Economic Costs, from Individuals to Health Systems: Evidence from a Middle-Income Country,” in The Diabetes Textbook, Springer, 2019, pp. 23-32.

J. R. B. Evia, “Mí©xico y el reto de las enfermedades crónicas no transmisibles. El laboratorio tambií©n juega un papel importante,” Rev. Mex. Patol. Clí­nica y Med. Lab., vol. 65, no. 1, pp. 4-17, 2018.

E. Y. Mendoza-López, V. Zavala-Ibarra, M. V. Sí¡nchez-Gonzí¡lez, S. M. López-Correa, and J. Carranza-Madrigal, “Detección de diabetes mellitus en adolescentes con sobrepeso y obesidad,” Med. Interna Mí©xico, vol. 32, no. 1, pp. 9-13, 2016.

R. A. Gómez-Dí­az et al., “Incidence of type 1 diabetes in Mexico: data from an institutional register 2000-2010,” Diabetes Care, vol. 35, no. 11, pp. e77-e77, 2012.

D. Medina and L. Moreno, Encuesta Nacional de Salud y Nutrición 2018 “Boletí­n tí©cnico N°-XX-Año-OE,” 2019 website [Online]. Available: https://ensanut.insp.mx/

D. Gaytí¡n-Herní¡ndez, S. O. Gutií©rrez-Enrí­quez, A. Dí­az-Oviedo, C. E. Gonzí¡lez-Acevedo, M. Miranda-Herrera, and L. E. Herní¡ndez-Ibarra, “Escenario futuro de la diabetes mellitus tipo 2 estimado con un modelo de simulación diní¡mico predictivo,” Rev. Panam. Salud Píºblica, vol. 41, p. e93, 2018.

A. Izquierdo-Valenzuela, X. Boldo-León, and J. M. Muñoz-Cano, “Riesgo para desarrollar diabetes mellitus tipo 2 en una comunidad rural de Tabasco,” Salud en Tabasco, vol. 16, no. 1, pp. 861-868, 2010.

S. Plan, “2020 - 2030 Strategic Plan for,” 2020.

W. Y. Stephanie, C. Hill, M. L. Ricks, J. Bennet, and N. E. Oriol, “The scope and impact of mobile health clinics in the United States: a literature review,” Int. J. Equity Health, vol. 16, no. 1, pp. 1-12, 2017.

O. Y. Bello-Chavolla, R. Rojas-Martinez, C. A. Aguilar-Salinas, and M. Herní¡ndez-Avila, “Epidemiology of diabetes mellitus in Mexico,” Nutr. Rev., vol. 75, no. suppl_1, pp. 4-12, 2017.

L. A. DiMeglio, C. Evans-Molina, and R. A. Oram, “Type 1 diabetes,” Lancet, vol. 391, no. 10138, pp. 2449-2462, 2018.

C. T. Ferrara et al., “Excess BMI in childhood: a modifiable risk factor for type 1 diabetes development?,” Diabetes Care, vol. 40, no. 5, pp. 698-701, 2017.

A. Pitchika et al., “Associations of maternal diabetes during pregnancy with overweight in offspring: results from the prospective TEDDY study,” Obesity, vol. 26, no. 9, pp. 1457-1466, 2018.

G. Soto-Estrada, L. Moreno Altamirano, J. J. Garcí­a-Garcí­a, I. Ochoa Moreno, and M. Silberman, “Trends in frequency of type 2 diabetes in Mexico and its relationship to dietary patterns and contextual factors,” Gac. Sanit., vol. 32, pp. 283-290, 2018.

A. Fehr, C. Lange, J. Fuchs, H. Neuhauser, and R. Schmitz, “Health monitoring and health indicators in Europe,” pp. 3-21, 2017, doi: 10.17886/RKI-GBE-2017-020.

C. P. R. López et al., “Metabolic syndrome, physical activity and eating habits in school children of the south of Mexico City.,” Invest. Clin., vol. 60, no. 1, pp. 7-19, 2019.

OECD, health at a Glance 2017:Indicators. Paris: OECD, 2017.

S. Social, “Chí©cate , Mí­dete , Muí©vete,” pp. 1-2, 2021.

M. í. G. Block, H. R. Morales, L. C. Hurtado, A. Balandrí¡n, E. Mí©ndez, and W. H. Organization, “Mexico: health system review,” 2020.

C. R. Valle, A. M. B. Ojeda, and J. R. G. Níºñez, “Intervención educativa y valoración familiar de enfermerí­a utilizando el Modelo Calgary en una familia de Culiací¡n, Sinaloa, Mí©xico,” Revista de Investigación Transdisciplinaria en Educación, Empresa y Sociedad - ITEES, vol. 3, no. 3, pp 318-335, 2020.

X. Huo et al., “Risk of non-fatal cardiovascular diseases in early-onset versus late-onset type 2 diabetes in China: a cross-sectional study,” lancet Diabetes Endocrinol., vol. 4, no. 2, pp. 115-124, 2016.

A. M. Egan et al., “Epidemiology of gestational diabetes mellitus according to IADPSG/WHO 2013 criteria among obese pregnant women in Europe,” Diabetologia, vol. 60, no. 10, pp. 1913-1921, 2017.

J. A. P. Gonzí¡lez and A. Vidal-Puig, “Obesity in the Pathophysiology of Diabetes,” in The Diabetes Textbook, Springer, 2019, pp. 185-213.

H. Bang et al., “Development and validation of a patient self-assessment score for diabetes risk,” Ann. Intern. Med., vol. 151, no. 11, pp. 775-783, 2009.

S. A. Rains, M. D. Hingle, M. Surdeanu, D. Bell, and S. Kobourov, “A test of the risk perception attitude framework as a message tailoring strategy to promote diabetes screening,” Health Commun., vol. 34, no. 6, pp. 672-679, 2019.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).