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Mathematical study of polycystic ovarian syndrome disease including medication treatment mechanism for infertility in women.
Batool, Maryam; Farman, Muhammad; Ahmad, Aqeel; Nisar, Kottakkaran Sooppy.
Afiliación
  • Batool M; Institute of Mathematics, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
  • Farman M; Institute of Mathematics, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
  • Ahmad A; Department of Computer Science and Mathematics, Lebanese American University, 1107-2020, Beirut, Lebanon.
  • Nisar KS; Faculty of Arts and science, Mathematical research center, Near East University, Northern Cyprus, Turkey.
AIMS Public Health ; 11(1): 19-35, 2024.
Article en En | MEDLINE | ID: mdl-38617407
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: AIMS Public Health Año: 2024 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: AIMS Public Health Año: 2024 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Estados Unidos