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Optimization of Modular Neural Networks for the Diagnosis of Cardiovascular Risk

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Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 940))

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Abstract

These days, with the current situation we are experiencing worldwide due to the pandemic, it is of utmost importance to know our state of health, speaking more specifically of our cardiovascular health. Soft computing can be used by medical experts as a powerful tool to help and facilitate providing a diagnosis of our state of health. The objective of this work is to create a modular neural network to obtain the risk diagnosis that a patient has in developing a cardiovascular event in a period of 10 years likewise, find the heart age. In order to provide this information, a series of risk factors will be given as input to each of the modules, such as age, gender, body mass index, systolic pressure, if the patient is diabetic, if the patient smokes, if he/she is under hypertension treatment. Each module is optimized with two bio-inspired algorithms to test its performance and thereby obtain the best results to provide an accurate diagnosis.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, BC, Mexico

    Ivette Miramontes, Patricia Melin, Oscar Carvajal & German Prado-Arechiga

Authors
  1. Ivette Miramontes
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  2. Patricia Melin
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  3. Oscar Carvajal
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  4. German Prado-Arechiga
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Corresponding author

Correspondence to Patricia Melin .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

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Miramontes, I., Melin, P., Carvajal, O., Prado-Arechiga, G. (2021). Optimization of Modular Neural Networks for the Diagnosis of Cardiovascular Risk. In: Castillo, O., Melin, P. (eds) Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-68776-2_6

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