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Optimization of Neural Network Models for Estimating the Risk of Developing Hypertension Using Bio-inspired Algorithms

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Fuzzy Information Processing 2020 (NAFIPS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1337))

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Abstract

Nowadays, the use of intelligent systems can help in achieving a quick and timely diagnosis, with the aim of avoiding or controlling some diseases. In this case, the general goal of this work is to provide an intelligent model capable of solving a real life health problem, such as the risk of developing hypertension. For this reason, a new computational model is proposed using a neural network that has the ability to estimate the risk of developing high blood pressure in the next four years, which is optimized using the Flower Pollination Algorithm and Ant Lion Optimizer. The neural network model has seven inputs that are: age, gender, body mass index, systolic pressure, diastolic pressure, if the patient smokes, and if the patient has parents with hypertension, and one output, which is the risk of developing hypertension in the next 4 years. Simulation results show the advantage of the proposed approach.

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Author information

Authors and Affiliations

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

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

Authors
  1. Patricia Melin
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  2. Ivette Miramontes
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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. Department of Mathematics, DigiPen Institute of Technology, Redmond, WA, USA

    Barnabás Bede

  2. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Martine Ceberio

  3. School of Engineering and Technology, University of Washington, Tacoma, WA, USA

    Martine De Cock

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

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Melin, P., Miramontes, I., Carvajal, O., Prado-Arechiga, G. (2022). Optimization of Neural Network Models for Estimating the Risk of Developing Hypertension Using Bio-inspired Algorithms. In: Bede, B., Ceberio, M., De Cock, M., Kreinovich, V. (eds) Fuzzy Information Processing 2020. NAFIPS 2020. Advances in Intelligent Systems and Computing, vol 1337. Springer, Cham. https://doi.org/10.1007/978-3-030-81561-5_19

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