Journal: Drug Invention Today

Article Id: JPRS-Other-00001940
Title: Mathematical modeling of the in-stent restenosis risk of patients with coronary heart disease
Category: Others ( DIT Related Science )
Section: Research Article
  • Abstract
  • Audio Abstract
  • Authors
  • Pdf File
  • Citation
  • My Reference
  • Methodology
  • Abstract

    Aims: This article presents the mathematical modeling of the in-stent restenosis risk of patients with coronary heart disease, which is also known as ischemic heart disease. Methods: The mathematical model was developed based on an artificial neural network and regression analysis. Results: The results identified increased in-stent restenosis risk in patients with the following genotypes: [Hp 1-2; Gc 1-2; Tf CB; C’s SS], [Hp 2-2; Gc 1-1; Tf CC; C’s FF], [Hp 2-2; Gc 1-2; Tf CB; C’s SS], [Hp 2-2; Gc 1-1; Tf CC; C’s SS]. Conclusion: The coefficients of determination of ANN (0.21) and linear regression (0.11) were calculated. The resulting values of the coefficient of determination were <0.5, suggesting that the simulation was unacceptable. Analysis revealed that the presence or absence of restenosis was possible with the same set of phenotypes

  • Abstract Audio

    No Audio file found

  • About the authors and Affiliations

    Author(s) Name:

    Stanislav I. Sivakov*, Valeriy M. Nikitin, Yuri I. Afanasiev, Konstantin I. Penzev and Svetlana Y Grigorova

    Affiliation(s) Name:

    Department of Informatics and Computational Technology, Belgorod State University, Belgorod, Russia

    *Corresponding author: Stanislav I. Sivakov, Department of Informatics and Computational Technology, Belgorod State University, Pobedy Street, 85, Belgorod, Russia.

  • View Article File in pdf format.

    Article File
  • View Article Citation Here.

    0 View More
  • How to Cite my Article.

    Author:

    Stanislav I. Sivakov*, Valeriy M. Nikitin, Yuri I. Afanasiev, Konstantin I. Penzev and Svetlana Y Grigorova

    Title:Mathematical modeling of the in-stent restenosis risk of patients with coronary heart disease
    Journal:Drug Invention Today
    Vol(issue):9 (December)
    Year:2017
    Page No: (44-47)
  • Experimental Methods Keywords

    Methodology:Mathematical modeling, Prognostication, Regression analysis, Restenosis
    Research Materials: Ischemic heart disease

Keywords

Artificial neural network Ischemic heart disease Mathematical modeling Prognostication Regression analysis Restenosis

Our Services

Most Downloaded List