Application of Genetic Algorithms for Medical Diagnosis of Diabetes Mellitus

Khushali Tyagi; Deepak Kumar; Richa Gupta

doi:10.52756/ijerr.2024.v37spl.001

Authors

Khushali Tyagi Department of Applied Sciences, Manav Rachna International Institute of Research and Studies http://orcid.org/0009-0000-9071-5146
Deepak Kumar Department of Applied Sciences, Manav Rachna International Institute of Research and Studies, Faridabad, India https://orcid.org/0000-0001-9419-8895
Richa Gupta Global Institute of Technology and Management, Gurgaon, India https://orcid.org/0009-0005-2134-8705

DOI:

https://doi.org/10.52756/ijerr.2024.v37spl.001

Keywords:

Diabetes Mellitus, Mathematical Model, Genetic Algorithm

Abstract

The system of glucose-insulin control and associated problems in diabetes mellitus were studied by mathematical modeling. It is a helpful theoretical tool for understanding the basic concepts of numerous distinct medical and biological functions. It delves into the various risk factors contributing to the onset of diabetes, such as sedentary lifestyle, obesity, family history, viruses, and increasing age. The study emphasizes the importance of mathematical models in understanding the dynamic characteristics of biological systems. The study emphasizes the increasing prevalence of diabetes, especially in India, where urbanization and lifestyle changes contribute to the rising incidence. The present investigation describes the use of John Holland's evolutionary computing approach and the Genetic Algorithm (GA) in diabetes mellitus. The Genetic Algorithm is applied to address issues related to diabetes, offering a generic solution and utilizing MATLAB's Genetic Algorithm tool. The Mathematical Model provides differential equations representing glucose and insulin concentrations in the blood. The results represent testing outcomes for normal, prediabetic, and diabetic individuals, optimized with Genetic Algorithm showcased through fitness value plots. The conclusion highlights the effectiveness of Genetic Algorithm as an optimization tool in predicting optimal samples for diabetes diagnosis. The paper encourages the use of heuristic algorithms, such as Genetic Algorithms, to address complex challenges in the field of diabetes research. Future scope includes further exploration of biomathematics and Genetic Algorithm applications for enhanced understanding and management of diabetes mellitus. It is critical for people with diabetes to consistently check their blood glucose levels and follow their treatment plan.

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