Machine Learning for Predictive Modelling of Healthcare-Associated Infections

Abstract

Healthcare-associated infections (HAIs) pose a significant threat to patient safety and increase healthcare costs. Predictive modeling using machine learning (ML) techniques offers a promising approach to prevent HAIs. This study develops ML models for predicting and preventing HAIs in hospitals. We utilize a dataset containing patient demographics, clinical variables, and infection outcomes. Various ML algorithms are trained and evaluated for their predictive performance. Our results show that [Insert findings and key results here]. This research contributes to the advancement of predictive modeling for HAIs and underscores the potential of ML in healthcare infection prevention.