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Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks

Author

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  • Vanessa Alcalá-Rmz

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico
    These authors contributed equally to this work.)

  • Laura A. Zanella-Calzada

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico
    These authors contributed equally to this work.)

  • Carlos E. Galván-Tejada

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico
    These authors contributed equally to this work.)

  • Alejandra García-Hernández

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico)

  • Miguel Cruz

    (Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Ciudad de México CP 06720, Mexico)

  • Adan Valladares-Salgado

    (Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Ciudad de México CP 06720, Mexico)

  • Jorge I. Galván-Tejada

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico)

  • Hamurabi Gamboa-Rosales

    (Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico)

Abstract

Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health conditions that involve low organ yield until the patient death. Based on this problem, this work proposes the architecture of an Artificial Neural Network (ANN) for the automated classification of healthy patients from diabetics patients. The analysis was performed used a set of 19 para-clinical features to determine the health status of the patients. The developed model was evaluated through a statistical analysis based on the calculation of the loss function, accuracy, area under the curve (AUC) and receiving operating characteristics (ROC) curve. The results obtained present statistically significant values, with accuracy of 0.94 and AUC values of 0.98. Based on these results, it is possible to conclude that the ANN implemented in this work can classify patients with presence of diabetes from controls with significant accuracy, presenting preliminary results for the development of a diagnostic tool that can be supportive for health specialists.

Suggested Citation

  • Vanessa Alcalá-Rmz & Laura A. Zanella-Calzada & Carlos E. Galván-Tejada & Alejandra García-Hernández & Miguel Cruz & Adan Valladares-Salgado & Jorge I. Galván-Tejada & Hamurabi Gamboa-Rosales, 2019. "Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks," IJERPH, MDPI, vol. 16(3), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:3:p:381-:d:201763
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    References listed on IDEAS

    as
    1. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    2. Claudine Irles & Gabriela González-Pérez & Sandra Carrera Muiños & Carolina Michel Macias & César Sánchez Gómez & Anahid Martínez-Zepeda & Guadalupe Cordero González & Estibalitz Laresgoiti Servitje, 2018. "Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors," IJERPH, MDPI, vol. 15(11), pages 1-18, November.
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