NOVEL ADAPTIVE AI-POWER INSULIN NAVIGATOR FOR DIABETES USING MACHINE LEARNING

Authors

  • Deekshith Alladi Department of Computer Science Chicago State University Sr. Research Scientist and Software Engineer Chicago, IL, USA.

DOI:

https://doi.org/10.26662/ijiert.v11i8.pp57-74

Keywords:

Adaptive AI, insulin delivery systems, diabetes management, continuous glucose monitoring, predictive analytics, glycemic control, personalized healthcare, real-time glucose monitoring, hypoglycemia prevention, automated insulin dosing, patient outcomes, AI-powered healthcare

Abstract

Adaptive AI-powered insulin delivery systems represent a significant advancement in diabetes management, offering enhanced precision and safety for patients with diabetes. This paper examines the development and implementation of AI-driven insulin delivery systems that dynamically adjust insulin dosages based on real-time glucose monitoring and predictive analytics. The system integrates continuous glucose sensors with adaptive AI algorithms to analyze blood glucose trends, activity levels, and dietary inputs, enabling the automatic adjustment of insulin delivery to meet individual patient needs. The study evaluates the performance of these systems in optimizing glycemic control, reducing the incidence of hypoglycemia and hyperglycemia, and improving overall patient outcomes. Key features include the ability to learn from historical data to refine dosing algorithms and the incorporation of safety mechanisms to prevent adverse events. Additionally, the paper explores user experiences and feedback, highlighting the system’s potential to improve quality of life and provide more personalized diabetes management. The integration of adaptive AI in insulin delivery systems promises to revolutionize diabetes care by offering a more responsive, accurate, and user-centric approach to insulin therapy.

Downloads

Published

2024-08-24

Issue

Vol. 11 No. 8 (2024): IJIERT

Section

Engineering and Technology

License

Copyright (c) 2024 International Journal of Innovations in Engineering Research and Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0 DEED).

You are free to:

  1. Share — copy and redistribute the material in any medium or format
  2. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. NonCommercial — You may not use the material for commercial purposes .
  3. NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
  4. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

 

Rights of Authors

Authors retain the following rights:
1. Copyright and other proprietary rights relating to the article, such as patent rights,
2. the right to use the substance of the article in future works, including lectures and books,
3. the right to reproduce the article for own purposes, provided the copies are not offered for sale,
4. the right to self-archive the article.