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The Use of Neural Networks for the Prediction of Type II Diabetes: A Comparison of Recent Advances and Perspectives

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Communication and Applied Technologies (ICOMTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 375))

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Abstract

The interest in the use of neural networks for predicting various diseases has increased in recent years. As type II diabetes is the leading cause of death worldwide, there is a particular emphasis on determining the best neural configurations that can provide high precision and accuracy. Since type II diabetes is considered an epigenetic disease, environmental mediated factors contributing to the development of this disease should be considered for the training of neural networks and its effects. This review article based on the Boolean method aims to compare various prediction methods using neural networks to determine the most favorable features that contribute to its better performance.

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Authors and Affiliations

  1. School of Mathematical and Computational Sciences, Yachay Tech University, Hacienda San José S/N, San Miguel de Urcuquí 100119, Urcuquí, Ecuador

    Mayra Tualombo, Luis D. Carlosama, Brandon A. J. Nieto, Carolina Cadena-Morejón & Andrés Tirado-Espín

  2. Language Department, Yachay Tech University, Hacienda San José S/N, San Miguel de Urcuquí 100119, Urcuquí, Ecuador

    David Montenegro-Montenegro

  3. School of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José S/N, San Miguel de Urcuquí 100119, Urcuquí, Ecuador

    Fernando Villalba-Meneses & Diego Almeida-Galárraga

  4. Universidad de Otavalo, Av. De los Sarances y Av. Los Pendoneros, Otavalo, Ecuador

    Diego Almeida-Galárraga & Andrés Tirado-Espín

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  1. Mayra Tualombo
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  2. Luis D. Carlosama
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  3. Brandon A. J. Nieto
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  7. Diego Almeida-Galárraga
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Corresponding author

Correspondence to Andrés Tirado-Espín .

Editor information

Editors and Affiliations

  1. Málaga, Spain

    Daniel Barredo Ibáñez

  2. Rúa da Maestranza, A Coruña, Spain

    Laura M. Castro

  3. Complejo Cultural Universitario, Puebla, Mexico

    Araceli Espinosa

  4. Circunvalación ao Campus Universitario, Vigo, Spain

    Iván Puentes-Rivera

  5. Campus Vida, Avenida do Doutor Ángel Jorge Echeverri, Santiago de Compostela, Spain

    Paulo Carlos López-López

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Tualombo, M. et al. (2024). The Use of Neural Networks for the Prediction of Type II Diabetes: A Comparison of Recent Advances and Perspectives. In: Ibáñez, D.B., Castro, L.M., Espinosa, A., Puentes-Rivera, I., López-López, P.C. (eds) Communication and Applied Technologies. ICOMTA 2023. Smart Innovation, Systems and Technologies, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-99-7210-4_4

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