Emergency Department Asthma Management: Harnessing Clustering Algorithms for Enhanced Precision Medicine

Precision medicine is essentially a modern approach to understanding health. Treatment decision support systems, or TDSSs, are a crucial part of modern health care methods. They are designed to close the knowledge gap between patients' initial knowledge and the comprehensive information required to make well-informed treatment decisions. Asthma remains a significant public health concern, particularly in the Emergency Department (ED) setting, where swift and accurate management is paramount. In our research we used clustering algorithms to scrutinize clustering accuracy, clinical significance, computational efficiency, and pinpointed the optimal approach with 88.43% of K-means Algorithm and 88.28% of Density-based Algorithm of correctly categorized instances. Based on accuracy and a balanced cluster distribution, Simple K-means and Density-based algorithms achieve more effective examples for individualized decision-making that produce informative outcomes. Through the application of advanced data analytics tools and precision medicine concepts, our research aims to optimize asthma management in the emergency department to streamline healthcare delivery and reduce costs. We seek to improve outcomes and reduce healthcare utilization by personalizing therapy recommendations for asthma patients by integrating clustering algorithms into treatment decision support systems (TDSS).