Artificial intelligence with machine learning and the enigmatic discovery of HIV cure
Abstract
HIV’s complexity has long presented a problem in the quest for a cure. However, the development of machine learning (ML) and artificial intelligence (AI) technology has opened up promising new directions for HIV cure research. This study investigates the impact of AI and ML on the discovery and development of an HIV cure to shed light on their potential role in hastening advancements in this field. The study employs quantitative methodology, and the execution of the methods is achieved by using AI and ML techniques for analysis processes and presenting the study’s findings by utilizing the Kaggle.com HIV dataset, where pertinent features are found for the machine learning algorithm. Additionally, advanced statistical techniques, such as Structural Equation Modeling (SEM), to investigate the causal link between AI and ML utilization and the development of a cure for HIV is utilized. The robustness of the analysis is enhanced by using Penalized Ridge and Lasso Regressions. The study utilizes logistic regression as the machine learning model, and the mean square error is used to evaluate performance. Control variables, including the year, borough, the Uniform Hospitalization Fund (UHF) code, gender, age, race, concurrent diagnoses, percentage linked to care within three months, the prevalence of (People living with HIV/AIDS) PLWDHI, and percentage of viral suppression, deaths, death rate, and HIV-related death rate are all taken into consideration, to ensure a thorough analysis. This study finds that AI and ML are the future of the healthcare sector, providing promising opportunities for finding a cure for HIV and enhancing patient care. Further, the study confirms that new targets for HIV cure research can be found by utilizing AI and ML, and treatment outcomes and individualized treatment plans can also be developed. AI and ML can also enhance clinical trials, boost HIV prevention efforts, and lower the number of new infections.
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DOI: https://doi.org/10.32629/jai.v7i2.697
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