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Keshavarz, Masoume, Allahviranloo, Tofigh, Abbasbandy, Saeid, Modarressi, Mohammad. (1400). Analytical fuzzy solution of the ventricular pressure equation and prediction of the blood pressure. سامانه مدیریت نشریات علمی, 9(4), 919-939. doi: 10.22034/cmde.2020.34163.1563
Masoume Keshavarz; Tofigh Allahviranloo; Saeid Abbasbandy; Mohammad Hossein Modarressi. "Analytical fuzzy solution of the ventricular pressure equation and prediction of the blood pressure". سامانه مدیریت نشریات علمی, 9, 4, 1400, 919-939. doi: 10.22034/cmde.2020.34163.1563
Keshavarz, Masoume, Allahviranloo, Tofigh, Abbasbandy, Saeid, Modarressi, Mohammad. (1400). 'Analytical fuzzy solution of the ventricular pressure equation and prediction of the blood pressure', سامانه مدیریت نشریات علمی, 9(4), pp. 919-939. doi: 10.22034/cmde.2020.34163.1563
Keshavarz, Masoume, Allahviranloo, Tofigh, Abbasbandy, Saeid, Modarressi, Mohammad. Analytical fuzzy solution of the ventricular pressure equation and prediction of the blood pressure. سامانه مدیریت نشریات علمی, 1400; 9(4): 919-939. doi: 10.22034/cmde.2020.34163.1563

Analytical fuzzy solution of the ventricular pressure equation and prediction of the blood pressure

Computational Methods for Differential Equations
مقاله 1، دوره 9، شماره 4، دی 2021، صفحه 919-939    اصل مقاله (1.37 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/cmde.2020.34163.1563
نویسندگان
Masoume Keshavarz1؛ Tofigh Allahviranloo* 1، 2؛ Saeid Abbasbandy1، 3؛ Mohammad Hossein Modarressi4
1Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2Department of Mathematics, facullty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey.
3Department of Applied Mathematics, Faculty of Science, Imam Khomeini International University, Qazvin, 34149-16818, Iran.
4Department of Medical Genetics & Molecular Medicine, Tehran University of Medical Sciences, Tehran, Iran.
چکیده
The cardiovascular system is an extremely intelligent and dynamic system which adjusts its performance depending on the individual's physical and environmental conditions. Some of these physical and environmental conditions may create slight disruptions in the cardiovascular system leading to a variety of diseases. Since prevention has always been preferable to treatment, this paper examined the Instantaneous Pressure-Volume Relation (IPVR) and also the pressure of the artery root. Fuzzy mathematics as a powerful tool is used to evaluate and predict the status of an individual's blood pressure. The arterial pressure is modeled as a first-order fuzzy differential equation and an analytical solution for this equation is obtained and an example shows the behavior of the solution. The risk factors using fuzzy rules are assessed, which help diagnose the status of an individual's blood pressure. Using the outcome by drawing the individual's attention to these risk factors, the individual's health is improved. Moreover, in this study, adaptive neuro-fuzzy inference system (ANFIS) models are evaluated to predict the status of an individual's blood pressure on the basis of the inputs.
کلیدواژه‌ها
Fuzzy instant pressure volume؛ Generalized Hukuhara difference؛ Generalized Hukuhara differentiable؛ Characterization theorem؛ Fuzzy output blood ow theorem؛ Fuzzy blood flow back theorem؛ Fuzzy model of arterial pressure؛ Adaptive neuro-fuzzy inference system
مراجع
  • [1]                   M. S. Aabdeh, J. Habibi, and E. Soroush, Induction of fuzzy classification systems via evolutionaryaco-basedalgorithms, International Journal of Simulation Systems, Science and Technology, 9 (2008), 1-8.
  • [2]                   T. Allahviranloo, M. Keshavarz, and Sh. Islam, The prediction of cardiovascular disorders by fuzzy difference equations, IEEE International Conference on Fuzzy Systems, (2016), , 1465- 1472.
  • [3]                   HM. Azamathulla, CK. Chang, A. Ab Ghani, J. Ariffin, NA. Zakaria, and Z. Abu Hasan, An ANFIS-based approach for predicting the bed load for moderately sized rivers. J Hydro-environ Res, 3 (2009), 35-44.
  • [4]                   B. Bede, Mathematics of fuzzy sets and fuzzy logic, Springer, London, (2013).
  • [5]                   B. Bede, Note on Numerical solutions of fuzzy differential equations by predictor corrector method, Information Sciences, 178 (2008), 1917-1922.
  • [6]                   B. Bede and L. Stefanini, Generalized differentiability of fuzzy-valued functions, Fuzzy Sets and Systems, 230 (2013), 119-141.
  • [7]                   T. Bekat, M. Erdogan, F. Inal, and A. Genc. Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks, 45 (2012), 882-887.
  • [8]                   J. J. Buckley and Y. Qu, Solving fuzzy equations: a new concept, Fuzzy Sets and Systems, 39 (1991), 291-301.
  • [9]                   D. Burkhoff, I. Mirsky, and H. Suga, Assessment of systolic and diastolic ventricular properties via pressure-volume analysis: a guide for clinical, translational, and basic researchers. Am. J. Physiol. Heart Circ. Physiol, 289 (2005), H501-H512.
  • [10]                 M. Friedman, M. Ma, and A. Kandel, Numerical solutions of fuzzy differential and integral equations, in: Fuzzy Modeling and Dynamics, Fuzzy Sets Syst, 106 (1999), 3548.
  • [11]                  R. Goetschel and W. Voxman, Elementary fuzzy calculus, Fuzzy Sets and Systems, 24 (1987), 3143.
  • [12]                 Y. C. Hu, Sugeno fuzzy integral for finding fuzzy if-then classification rules, Applied Mathe- matics and Computation, 185 (2007), 72-83.
  • [13]                 M. Hukuhara, Integration des applications measurables dont la valeur est un compact convexe, Funkcialaj Ekvacioj, 10 (1967), 205-223.
  • [14]                 O. Kaleva and S. Seikkala, On fuzzy metric spaces, Fuzzy Sets and Systems, 12 (1984), 215-229.
  • [15]                 A. Kaufmann and M. M. Gupta, Introduction Fuzzy Arithmetic, Van Nostrand Reinhold, New York, (1985).
  • [16]                 M. Keshavarz, T. Allahviranloo, S. Abbasbandy, and M. H. Modarressi, A Study of Fuzzy Methods for Solving System of Fuzzy Differential Equations, New Mathematics and Natural Computation, 2020, In Press.
  • [17]                 B. Khoshnevisan, Sh. Rafiee, M. Omid, and H. Mousazadeh, Development of an intelligent sys- tem based on ANFIS for predicting wheat grain yield on the basis of energy inputs, Information processing in agriculture, 1 (2014), 14-22.
  • [18]                 V. Lakshmikantham, T. Bhaskar, and J. Devi, Theory of Set Differential Equations in Metric Spaces, Cambridge Scientific Publishers, (2006).
  • [19]                 J. W. Lankhaar, F. A. Rovekamp, P. Steendijk, T. J. C. Faes, B. E. Westerhof,  T. Kind,  A.  Vonk noordegraaf, and N. Westerhof, Modeling the instantaneous pressure-volume relation of the left ventricle: A comparison of six models, Annals of Biomedical Engineering, 37 (2009), 1710-1726.
  • [20]                 A. Lekova, L. Mikhailov, D. Boyadjiev, and A. Nabout, Redundant fuzzy rules exclusion by genetic algorithms, Fuzzy Sets and Systems, 100 (1998), 235-243.
  • [21]                 Math Works, Fuzzy logic toolbox user’s guide, Natick. Inc, 3 (2012), 137-179.
  • [22]                 E. G. Mansoori, M. J. Zolghadri, and S. D. Katebi, Using distribution of data to enhance performance of fuzzy classification systems, Iranian Journal of Fuzzy Systems, 4 (2007), 21-36.
  • [23]                 E. G. Mansoori, M. J. Zolghadri, and S. D. Katebi, Aweighting function for improving fuzzy classification systems performance, Fuzzy Sets and Systems, 158 ( 2007), 583-591.
  • [24]                 R. A. Mohammadpour, S. Mohammad Abedi, S. Bagheri, and A. Ghaemian, Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease, Hindawi Publishing Corporation, Computational and Mathematical Methods in Medicine, 2015 ( 2015), 1-8.
  • [25]                 J. T. Ottesen, M. S. Olufsen, and J. K. Larsen, Applied mathematical models in human physi- ology, Society for Industrial and Applied Mathematics Philadelphia, (1959).
  • [26]                 D. Petkovic, N. Pavlovi, Sh. Shamshirband, M. L. Kiah, N. B. Anuar, and M. Y. Idna Idris, Adaptive neuro-fuzzy estimation of optimal lens system parameters, Opt Lasers Eng, 55 (2014), 84-93.
  • [27]                 E. Rezaei, A. Karami, T. Yousefi and S. Mahmoudinezhac, Modeling the free convection heat transfer in a partitioned cavity using ANFIS, Int Commun Heat Mass Transfer, 39 (2012), 470-475.
  • [28]                 L. Stefanini, A generalization of Hukuhara difference and division for interval and fuzzy arith- metic, Fuzzy Sets System, 161 (2010), 1564-1584.
  • [29]                 L. Stefanini and B. Bede, Generalized Hukuhara differentiability of interval-valued functions and interval differential equations, Nonlinear Analysis, 71 (2009), 1311-1328.
  • [30]                 Z. Yang, Y. Liu, and C. Li, Interpolation of missing wind data based on ANFIS, Renewable Energy, 36 (2011), 993-998.
  • [31]                 L. A. Zadeh, Information and Computation, Fuzzy sets, 8 (1965), 338-353.
  • [32]                 Z. Zhao, TL. Chow, HW. Rees, Q. Yang, Z. Xing, and FR. Meng,Predict soil texture distributions using an artificial neural network model, Comput Electron Agric, 65 (2009), 36-48.
  • [33]                 H. J. Zimmermann, Fuzzy Sets Theory and Applications, Kluwer, Dorrecht, (1985).
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