Linear and non-linear mathematical model of the physiological behavior of diabetes
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.spl.45Keywords:
Typical roots approach, The Direct Method of Lyapunov, Diabetes.Dimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This paper’s goals center on understanding the physiological behavior of diabetes, specifically type 2 diabetes, through mathematical modeling and in order to assess the health of diabetic patients and identify the most effective and practical blood glucose control strategies. Additionally, research on diabetes patients, both those with and without complications, is the main objective. Either a new model can be built or an existing model can be improved in order to develop a mathematical model for diabetes mellitus.Abstract
How to Cite
Downloads
Similar Articles
- V. Manikandabalaji, R. Sivakumar, V. Maniraj, A novel approach using type-II fuzzy differential evolution is proposed for identifying and diagnosis of diabetes using semantic ontology , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- V. Manikandabalaji, R. Sivakumar, V. Maniraj, A framework for diabetes diagnosis based on type-2 fuzzy semantic ontology approach , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Lavkush Pandey, Trilokinath, Convergence of Bisection Method , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- T. Ramyaveni, V. Maniraj, Hyperparameter tuning of diabetes prediction using machine learning algorithm with pelican optimization algorithm , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- A. Kalaiselvi, A. Chandrabose, Fuzzy logic-driven scheduling for cloud computing operations: a dynamic and adaptive approach , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Raja S, Nagarajan L., Hybridization of bio-inspired algorithms with machine learning models for predicting the risk of type 2 diabetes mellitus , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Avdhesh Kumar, Manoj Agarwal, Studies on challenges and opportunities for foreign direct investment in the automobile industry in India , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Rahul Maurya, Thirupataiah B, Lakshminarayana Misro, Thulasi R, Effect of the Solvent Polarity and Temperature in the Isolation of Pure Andrographolide from Andrographis paniculata , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Lavkush Pandey, Trilokinath, Convergence of the Method of False Position , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Krishna P. Kalyanathaya, Krishna Prasad K, A novel method for developing explainable machine learning framework using feature neutralization technique , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
You may also start an advanced similarity search for this article.