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Research article

Mathematical study of polycystic ovarian syndrome disease including medication treatment mechanism for infertility in women

  • Received: 09 September 2023 Accepted: 16 November 2023 Published: 04 December 2023
  • Among women of reproductive age, PCOS (polycystic ovarian syndrome) is one of the most prevalent endocrine illnesses. In addition to decreasing female fertility, this condition raises the risk of cardiovascular disease, diabetes, dyslipidemia, obesity, psychiatric disorders and other illnesses. In this paper, we constructed a fractional order model for polycystic ovarian syndrome by using a novel approach with the memory effect of a fractional operator. The study population was divided into four groups for this reason: Women who are at risk for infertility, PCOS sufferers, infertile women receiving therapy (gonadotropin and clomiphene citrate), and improved infertile women. We derived the basic reproductive number, and by utilizing the Jacobian matrix and the Routh-Hurwitz stability criterion, it can be shown that the free and endemic equilibrium points are both locally stable. Using a two-step Lagrange polynomial, solutions were generated in the generalized form of the power law kernel in order to explore the influence of the fractional operator with numerical simulations, which shows the impact of the sickness on women due to the effect of different parameters involved.

    Citation: Maryam Batool, Muhammad Farman, Aqeel Ahmad, Kottakkaran Sooppy Nisar. Mathematical study of polycystic ovarian syndrome disease including medication treatment mechanism for infertility in women[J]. AIMS Public Health, 2024, 11(1): 19-35. doi: 10.3934/publichealth.2024002

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  • Among women of reproductive age, PCOS (polycystic ovarian syndrome) is one of the most prevalent endocrine illnesses. In addition to decreasing female fertility, this condition raises the risk of cardiovascular disease, diabetes, dyslipidemia, obesity, psychiatric disorders and other illnesses. In this paper, we constructed a fractional order model for polycystic ovarian syndrome by using a novel approach with the memory effect of a fractional operator. The study population was divided into four groups for this reason: Women who are at risk for infertility, PCOS sufferers, infertile women receiving therapy (gonadotropin and clomiphene citrate), and improved infertile women. We derived the basic reproductive number, and by utilizing the Jacobian matrix and the Routh-Hurwitz stability criterion, it can be shown that the free and endemic equilibrium points are both locally stable. Using a two-step Lagrange polynomial, solutions were generated in the generalized form of the power law kernel in order to explore the influence of the fractional operator with numerical simulations, which shows the impact of the sickness on women due to the effect of different parameters involved.




    Acknowledgments



    This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).

    Conflict of interest



    The authors declare no conflict of interest.

    [1] Vikas B, Anuhya B S, Chilla M, et al. (2018) A critical study of Polycystic Ovarian Syndrome (PCOS) classification techniques. Int J Comput Eng Manag 4: 1-7.
    [2] Alamoudi A, Khan I U, Aslam N, et al. (2023) A deep learning fusion approach to diagnosis the polycystic ovary syndrome (PCOS). Appl Comput Intell S 2023: 1-15. https://doi.org/10.1155/2023/9686697
    [3] Saravanan A, Sathiamoorthy S (2018) Detection of polycystic ovarian syndrome: A literature Survey. Asian J Eng Appl Technol 7: 46-51. https://doi.org/10.51983/ajeat-2018.7.2.1008
    [4] Chouhan A S, Dadhich R (2022) Advance research on polycystic ovary syndrome (PCOS). J Womens Healthcare Midwifery Res 106: 2-5. https://doi.org/10.47363/JWHMR/2022(1)106
    [5] Chaudhuri A (2023) Polycystic ovary syndrome: Causes, symptoms, pathophysiology, and remedies. Obesity Med 39: 100480. https://doi.org/10.1016/j.obmed.2023.100480
    [6] Krishnaveni V (2019) A roadmap to a clinical prediction model with computational intelligence for pcos. Int J Manage Technol Eng 9: 177-185. https://doi.org/10.1016/j.obmed.2023.100480
    [7] Hafezi S G, Zand M A, Molaei M, et al. (2019) Dynamic model with factors of polycystic ovarian syndrome in infertile women. Int J Reprod BioMed 17: 231. https://doi.org/10.18502/ijrm.v17i4.4548
    [8] Deswal R, Narwal V, Dang A, et al. (2020) The prevalence of polycystic ovary syndrome: A brief systematic review. J Hum Reprod Sci 13: 261-271. https://doi.org/10.4103/jhrs.JHRS9518
    [9] Dokuyucu M A, Dutta H (2020) Analytical and numerical solutions of a TB-HIV/AIDS co-infection model via fractional derivatives without singular kernel. Math Modelling Analysis Infect Dis 2020: 181-212. https://doi.org/10.1007/978-3-030-49896-27
    [10] Khan T, Rihan F A, Ahmad H (2023) Modelling the dynamics of acute and chronic hepatitis B with optimal control. Sci Rep 13: 14980. https://doi.org/10.1038/s41598-023-39582-9
    [11] Shehata M S, Ahmad H, Zahran E H, et al. (2023) Isomorphic shut form valuation for quantum field theory and biological population models. Open Phys 21: 20220252. https://doi.org/10.1515/phys-2022-0252
    [12] Haq I U, Ali N, Ahmad H, et al. (2023) Mathematical analysis of a Corona virus model with Caputo, Caputo-Fabrizio-Caputo fractional and Atangana-Baleanu-Caputo differential operators. Int J Biomath . https://doi.org/10.1142/S1793524523500857
    [13] Almutairi N, Saber S, Ahmad H (2023) The fractal-fractional Atangana-Baleanu operator for pneumonia disease: stability, statistical and numerical analyses. AIMS Math 8: 29382-29410. https://doi.org/10.3934/math.20231504
    [14] Jain R, Mehta R, Sharma M K, et al. (2023) Numerical analysis of heat and mass transport of hybrid nanofluid over an extending plate with inclined magnetic field in presence of Soret and dufour Effect. Mod Phys Lett B 2450037. https://doi.org/10.1142/S0217984924500374
    [15] Saifullah S, Ali A, Irfan M, et al. (2021) Time-fractional Klein–Gordon equation with solitary/shock waves solutions. Math Probl Eng 2021: 1-15. https://doi.org/10.1155/2021/6858592
    [16] Khater M M (2023) Characterizing shallow water waves in channels with variable width and depth; computational and numerical simulations. Chaos, Solitons Fractals 173: 113652. https://doi.org/10.1016/j.chaos.2023.113652
    [17] Khater M M (2023) Long waves with a small amplitude on the surface of the water behave dynamically in nonlinear lattices on a non-dimensional grid. Int J Mod Phys B 37: 2350188. https://doi.org/10.1142/S0217979223501886
    [18] Khater M M (2023) Soliton propagation under diffusive and nonlinear effects in physical systems;(1+ 1)–dimensional MNW integrable equation. Phys Lett A 2023: 128945. https://doi.org/10.1016/j.physleta.2023.128945
    [19] Han T, Khater M M (2023) Numerical and computational investigation of soliton propagation in physical systems via computational schemes:(1+ 1)–dimensional MNW integrable equation. Results Phys 2023: 106567. https://doi.org/10.1016/j.rinp.2023.106567
    [20] Khater M M (2023) Advancements in computational techniques for precise solitary wave solutions in the (1+ 1)-dimensional mikhailov-novikov-wang equation. Int J Theor Phys 62: 152. https://doi.org/10.1007/s10773-023-05402-z
    [21] Khater M, Xia Y, Zhang X, et al. (2023) Investigating soliton dynamics: Contemporary computational and numerical approaches for analytical and approximate solutions of the CDG model. AIP Adv 13: 7. https://doi.org/10.1063/5.0154040
    [22] Khater M M (2023) Computational simulations of propagation of a tsunami wave across the ocean. Chaos, Solitons Fractals 174: 113806. https://doi.org/10.1016/j.chaos.2023.113806
    [23] Jajarmi A, Baleanu D, Zarghami Vahid K, et al. (2022) A general fractional formulation and tracking control for immunogenic tumor dynamics. Math Meth Appl Sci 45: 667-680. https://doi.org/10.1002/mma.7804
    [24] Naik P A, Zu J, Naik M U D (2021) Stability analysis of a fractional-order cancer model with chaotic dynamics. Int J Biomath 14: 2150046. https://doi.org/10.1142/S1793524521500467
    [25] Atangana A (2017) Fractal-fractional differentiation and integration: connecting fractal calculus and fractional calculus to predict complex system. Chaos, solitons fractals 102: 396-406. https://doi.org/10.1016/j.chaos.2017.04.027
    [26] Saleem M U, Farman M, Ahmad A, et al. (2020) A Caputo Fabrizio fractional order model for control of glucose in insulin therapies for diabetes. Ain Shams Eng J 11: 1309-1316. https://doi.org/10.1016/j.asej.2020.03.006
    [27] Rezapour S, Etemad S, Avcı İ, et al. (2022) A study on the fractal-fractional epidemic probability-based model of SARS-CoV-2 virus along with the Taylor operational matrix method for its Caputo version. J Funct Space 2022. https://doi.org/10.1155/2022/2388557
    [28] Farman M, Akgül A, Abdeljawad T, et al. (2022) Modeling and analysis of fractional order Ebola virus model with Mittag-Leffler kernel. Alex Eng J 61: 2062-2073. https://doi.org/10.1016/B978-0-323-99888-8.00010-3
    [29] Khan F S, Khalid M, Bazighifan O, et al. (2022) Euler's numerical method on fractional DSEK model under ABC derivative. Complexity 2022: 1-12. https://doi.org/10.1155/2022/4475491
    [30] Asamoah J K K (2022) Fractal–fractional model and numerical scheme based on Newton polynomial for Q fever disease under Atangana–Baleanu derivative. Results Phys 34: 105189. https://doi.org/10.1016/j.rinp.2022.105189
    [31] Farman M, Shehzad A, Akgül A, et al. (2023) Modelling and analysis of a measles epidemic model with the constant proportional Caputo operator. Symmetry 15: 468. https://doi.org/10.2139/ssrn.4506490
    [32] Baleanu D, Hasanabadi M, Vaziri A M, et al. (2023) A new intervention strategy for an HIV/AIDS transmission by a general fractional modeling and an optimal control approach. Chaos, Solitons Fractals 167: 113078. https://doi.org/10.1016/j.chaos.2022.113078
    [33] Farman M, Malik S M, Akgül A, et al. (2023) Analysis and dynamical transmission of tuberculosis model with treatment effect by using fractional operator. Preprint 1-18. https://doi.org/10.21203/rs.3.rs-2438955/v1
    [34] Zhang X, Niu P, Ma Y, et al. (2017) Global Mittag-Leffler stability analysis of fractional-order impulsive neural networks with one-side Lipschitz condition. Neural Networks 94: 67-75. https://doi.org/10.1016/j.neunet.2017.06.010
    [35] Margulies E H (2001) A comprehensive bioinformatics approach toward the molecular characterization of vertebrate limb specification and development [Master's thesis]. [USA]: University of Michigan 24 p.
    [36] Li J, Liu D, Li M (2023) Probabilistic response analysis of nonlinear vibro-impact systems with two correlated Gaussian white noises. Int J Nonlin Mech 151: 104370. https://doi.org/10.1016/j.ijnonlinmec.2023.104370
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