Abstract

Hidden Markov Model Integration with Queuing Theory in Artificial Intelligence: A Noval Algorithm in Healthcare Systems

Artificial Intelligence (AI) has significantly transformed various industries by offering innovative solutions to intricate problems. This paper explores the integration of Hidden Markov Models (HMMs) and Queuing theory, an area of mathematics dealing with waiting lines and service processes, to augment AI systems' capabilities. We delve into the fundamental concepts of HMMs and Queuing theory and examine how their combined application can address real-world challenges effectively. The healthcare sector is confronted with various challenges, particularly in resource-constrained environments, when it comes to delivering efficient and timely services to patients. This research investigates the utilization of Hidden Markov Models (HMMs) in conjunction with Queuing theory as a means to tackle these issues. The study demonstrates the potential of integrating these two techniques to optimize healthcare processes, minimize patient waiting times, and enhance overall healthcare delivery. The validation of this approach involves simulations and a thorough analysis of results, underscoring the promising benefits it can bring to healthcare management.

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