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Hybrid Neural-Fuzzy Modeling and Classification System for Blood Pressure Level Affectation

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

Part of the book series: Studies in Computational Intelligence ((SCI,volume 827))

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Abstract

In the recent years, the technological advancements tend to leave people with the necessity to stay in a sedentary position for several hours in order to achieve their work objectives. One of the parameters involving health issues tends to do with blood pressure. These are measurements that involve the circulation of the blood throughout the human body. We are developing a modeling and classification of hybrid system to evaluate and see the tendency of how much does a sedentary state affect while working in front of a computer for more than 8 h. The main idea is that the system learns the behavior or the trend of the different blood pressure that are extracted from individuals that fit this experiment. A modular network will be used for the part of modeling, while using fuzzy module to help us determine the best practical classification process in order to obtain a more precise affection of blood pressure.

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References

  1. P. Melin, G. Prado-Arechiga, Fuzzy logic for arterial hypertension classification, in New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension (Springer International Publishing, Berlin, 2018), pp. 5–13

    Google Scholar 

  2. O. Otto (ed.) Information and Classification. ISBN 978-3-642-50974-2, XI (1993), p. 517

    Google Scholar 

  3. A. Shigeo, Pattern classification. 2001. Massimo Volpe. High Blood Pressure & Cardiovascular Prevention. ISSN: 1120-9879 (print version). Journal no. 40292

    Google Scholar 

  4. P. Melin, O. Castillo, Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing (Springer, Berlin, 2005)

    Book  Google Scholar 

  5. R. Alhajj, U.K. Wiil, Network Modeling Analysis in Health Informatics and Bioinformatics. ISSN: 2192-6670 (electronic version) Journal no. 13721

    Google Scholar 

  6. F. Thomas, J. Blacher, A. Benetos, M.E. Safar, B. Pannier, J. Hypertens. 26(6), 1072–1077 (2008)

    Google Scholar 

  7. S. Das, P.K. Ghosh, Hypertension diagnosis: a comparative study using fuzzy expert system and neuro fuzzy system, in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Hyderabad, India (7–10 July 2013), pp. 1–7

    Google Scholar 

  8. M. Neshat, M. Yaghobi, M.B. Naghibi, A. Esmaelzadeh, Fuzzy expert system design for diagnosis of liver disorders, in Proceedings of the 2008 KAM’08 International Symposium on Knowledge Acquisition and Modeling, Wuhan, China (21–22 December 2008), pp. 252–256

    Google Scholar 

  9. A.A. Abdullah, Z. Zakaria, N.F. Mohammad, Design and development of fuzzy expert system for diagnosis of hypertension, in Proceedings of the 2011 Second International Conference on Intelligent Systems, Modeling and Simulation (ISMS), Kuala Lumpur, Malaysia (25–27 January 2011), pp. 113–117

    Google Scholar 

  10. P. Melin, C. Gonzalez, D. Bravo, F. Gonzalez, G. Martinez, Modular neural networks and fuzzy Sugeno integral for pattern recognition: the case of human face and fingerprint, in Hybrid Intelligent Systems. Studies in Fuzziness and Soft Computing, vol. 208, ed. by O. Castillo, P. Melin, J. Kacprzyk, W. Pedrycz (Springer, Berlin, Heidelberg, 2007)

    Google Scholar 

  11. I. Leal, P. Melin, Time series forecasting of tomato prices and processing in parallel in Mexico using modular neural networks, in Hybrid Intelligent Systems. Studies in Fuzziness and Soft Computing, vol. 208, ed. by O. Castillo, P. Melin, J. Kacprzyk, W. Pedrycz (Springer, Berlin, Heidelberg, 2007)

    Google Scholar 

  12. J. Amezcua, P. Melin, Optimization of the LVQ network architectures with a modular approach for arrhythmia classification, in Novel Developments in Uncertainty Representation and Processing. Advances in Intelligent Systems and Computing, vol. 401, ed. by K. Atanassov, et al. (Springer, Cham, 2016)

    Google Scholar 

  13. A. Uriarte, P. Melin, F. Valdez, Optimization of modular neural network architectures with an improved particle swarm optimization algorithm, in Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol. 361, ed. by L. Zadeh, R. Yager, S. Shahbazova, M. Reformat, V. Kreinovich (Springer, Cham, 2018)

    Google Scholar 

  14. F. Gaxiola, P. Melin, F. Valdez, J.R. Castro, Person recognition with modular deep neural network using the iris biometric measure, in Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol. 749, ed. by O. Castillo, P. Melin, J. Kacprzyk (Springer, Cham, 2018)

    Google Scholar 

  15. D. Sánchez, P. Melin, J. Carpio, H. Puga, Comparison of optimization techniques for modular neural networks applied to human recognition, in Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol. 667, ed. by P. Melin, O. Castillo, J. Kacprzyk (Springer, Cham, 2017)

    Google Scholar 

  16. S. Sotirov, E. Sotirova, P. Melin, O. Castilo, K. Atanassov, Modular neural network preprocessing procedure with intuitionistic fuzzy intercriteria analysis method, in Flexible Query Answering Systems 2015. Advances in Intelligent Systems and Computing, vol. 400, ed. by T. Andreasen, et al. (Springer, Cham, 2016)

    Google Scholar 

  17. M. Pulido, P. Melin, G. Prado-Arechiga, A new model based on a fuzzy system for arterial hypertension classification, in Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol. 749, ed. by O. Castillo, P. Melin, J. Kacprzyk (Springer, Cham, 2018)

    Google Scholar 

  18. J.C. Guzmán, P. Melin, G. Prado-Arechiga, Fuzzy optimized classifier for the diagnosis of blood pressure using genetic algorithm, in Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol. 749, ed. by O. Castillo, P. Melin, J. Kacprzyk (Springer, Cham, 2018)

    MATH  Google Scholar 

  19. F. Thomas, J. Blacher, A. Benetos, M.E. Safar, B. Pannier, Cardiovascular risk as defined in the 2003 European blood pressure classification: the assessment of an additional predictive value of pulse pressure on mortality. J. Hypertens 26(6), 1072–1077 (2008)

    Article  Google Scholar 

  20. P. Melin, Image processing and pattern recognition with Mamdani interval type-2 fuzzy inference systems, in Combining Experimentation and Theory. Studies in Fuzziness and Soft Computing, vol. 271, ed. by E. Trillas, P. Bonissone, L. Magdalena, J. Kacprzyk (Springer, Berlin, Heidelberg, 2012)

    Google Scholar 

  21. B. González, P. Melin, F. Valdez, G. Prado-Arechiga, Ensemble neural network optimization using a gravitational search algorithm with interval type-1 and type-2 fuzzy parameter adaptation in pattern recognition applications, in Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol. 749, ed. by O. Castillo, P. Melin, J. Kacprzyk (Springer, Cham, 2018)

    Google Scholar 

  22. P. Melin, V. Ochoa, L. Valenzuela, G. Torres, D. Clemente, Modular neural networks with fuzzy Sugeno integration applied to time series prediction, in Hybrid Intelligent Systems. Studies in Fuzziness and Soft Computing, vol. 208, ed. by O. Castillo, P. Melin, J. Kacprzyk, W. Pedrycz (Springer, Berlin, Heidelberg, 2007)

    Google Scholar 

  23. R. Poli, S. Cagnoni, R. Livi, G. Coppini, G. Valli, A neural network expert system for diagnosing and treating hypertension. Computer 24, 64–71 (1991)

    Article  Google Scholar 

  24. J.A. Staessen, J. Wang, G. Bianchi, W.H. Birkenhäger, Essential hypertension. Lancet 361, 1629–1641 (2003)

    Article  Google Scholar 

  25. J.C. Guzmán, P. Melin, G. Prado-Arechiga, Neuro-fuzzy hybrid model for the diagnosis of blood pressure, in Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol. 667, ed. by P. Melin, O. Castillo, J. Kacprzyk (Springer, Cham, 2017)

    MATH  Google Scholar 

  26. J.C. Guzmán, P. Melin, G. Prado-Arechiga, Design of a fuzzy system for diagnosis of hypertension, in Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol. 601, ed. by P. Melin, O. Castillo, J. Kacprzyk (Springer, Cham, 2015)

    Google Scholar 

  27. J.C. Guzmán, P. Melin, G. Prado-Arechiga, A proposal of a fuzzy system for hypertension diagnosis, in Novel Developments in Uncertainty Representation and Processing. Advances in Intelligent Systems and Computing, vol. 401, ed. by K. Atanassov, et al. (Springer, Cham, 2016)

    Google Scholar 

  28. P. Srivastava, A. Srivastava, A. Burande, A. Khandelwal, A Note on Hypertension Classification Scheme and Soft Computing Decision Making System. ISRN Biomath, 2013 (2013) https://doi.org/10.1155/2013/342970

    Article  Google Scholar 

  29. B.B. Sumathi, Pre-diagnosis of hypertension using artificial neural network. Glob. J. Comput. Sci. Technol. 11, 2 (2011)

    Google Scholar 

  30. Z. Abrishami, I. Azad, Design of a fuzzy expert system and a multi-layer neural network system for diagnosis of hypertension. Bull. Environ. Pharmacol. Life Sci. 4, 138–145 (2015)

    Google Scholar 

  31. P. Melin, C.I. González, J.R. Castro, O. Mendoza, O. Castillo, Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)

    Article  Google Scholar 

  32. P. Melin, I. Miramontes, G. Prado-Arechiga, A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis. Expert Syst. Appl. 107, 146–164 (2018)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, B.C., Mexico

    Martin Vázquez & Patricia Melin

  2. Excel Medical Center, Tijuana, Mexico

    German Prado-Arechiga

Authors
  1. Martin Vázquez
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  2. Patricia Melin
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  3. 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, Baja California, Mexico

    Oscar Castillo

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

    Patricia Melin

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Vázquez, M., Melin, P., Prado-Arechiga, G. (2020). Hybrid Neural-Fuzzy Modeling and Classification System for Blood Pressure Level Affectation. In: Castillo, O., Melin, P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_18

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