Optimizing HIV / AIDS Antiretroviral Therapy with Genetic Algorithm

By using Genetic Algorithms (GAs) to enhance the efficacy of antiretroviral therapy (ART) regimens, this investigation aims to transform HIV / AIDS treatment practices. An interpretivism philosophy serves as the foundation for the deductive approach, which uses a design for descriptive studies and a...

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Bibliographic Details
Main Authors: Rai, Anjani Kumar, Pundir, Sumit, Shrivastava, Anurag, Kumar, C Praveen, Mehta, Ankit, Singh, Pankaj
Format: Conference Proceeding
Language:English
Subjects:
antiviral combination
Computational modeling
computer model
GA-optimized
genetic algorithmic algorithms
Genetic algorithms
Human immunodeficiency virus
Immune system
mathematical framework
Mathematical models
Medical treatment
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Summary:By using Genetic Algorithms (GAs) to enhance the efficacy of antiretroviral therapy (ART) regimens, this investigation aims to transform HIV / AIDS treatment practices. An interpretivism philosophy serves as the foundation for the deductive approach, which uses a design for descriptive studies and additional data gathering. The project is organized around four main theme areas: validation as well as comparative evaluation, GA improvement of ART, modeling using computers for HIV / AIDS development, and viability as well as execution of customized medicine. To mimic the course of the disease, a mathematical model is constructed and integrated with particular to patient parameters. GAs is used to methodically investigate various treatment choices while taking into account variables including the amount of virus, CD4 population count, and medication resistance characteristics. Validation and a comparison compare GA-optimized treatments to traditional ART procedures while scrutinizing the model's forecasts against data from clinical studies. Clinical viability, patient compliance, savings, and medication resistance management are covered as practical factors for implementation. Despite being optimistic, this research admits its possible drawbacks, which include reliance on supplementary data and modeling difficulties for viral processes. Future research is advised to involve transdisciplinary collaboration, greater integration of information, and long-term clinical studies, including investigations of real-world deployment.
ISSN:2687-7767
DOI:10.1109/UPCON59197.2023.10434580