PSO-XnB: a proposed model for predicting hospital stay of CAD patients
Geetha Pratyusha Miriyala,
Arun Kumar Sinha
Abstract:Coronary artery disease poses a significant challenge in decision-making when predicting the length of stay for a hospitalized patient. This study presents a predictive model—a Particle Swarm Optimized-Enhanced NeuroBoost—that combines the deep autoencoder with an eXtreme gradient boosting model optimized using particle swarm optimization. The model uses a fuzzy set of rules to categorize the length of stay into four distinct classes, followed by data preparation and preprocessing. In this study, the dimension… Show more
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