ACO based Clinical Decision Support System for Better Medical Care
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Abstract
In the realm of healthcare, the utilization of clinical decision support systems (CDSSs) has become increasingly prevalent as a means of providing medical professionals with a computer-based tool that grants them access to pertinent data and expertise, thereby aiding in their ability to make informed clinical decisions. The potential applications of a CDSS are numerous, ranging from disease diagnosis and the creation of treatment programs, to patient progress monitoring. A crucial component of a CDSS is its knowledge base, which comprises the data utilized by the system to generate recommendations and provide feedback to healthcare providers. In an effort to enhance the knowledge base of a CDSS for a particular clinical condition, metaheuristic methods such as Ant Colony Optimization (ACO) can be employed to select the most suitable and applicable data. ACO facilitates the identification of the portion of a CDSS's knowledge base that is most likely to result in the optimal clinical decision, from among the vast array of data that it may contain. This study aims to explore the potential benefits of utilizing ACO methods in CDSSs for the betterment of patient care. The paper outlines the design and implementation of an ACO-based CDSS, which can offer tailored treatment plans for patients based on their medical histories and current condition.
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