Revolutionizing Intensive Care: A Machine Learning Based Approach for ICU Patients' In-Hospital Mortality Prediction

Early prediction of in-hospital mortality in Intensive Care Unit (ICU) patients is crucial for optimizing resource allocation, informing prognosis discussions, and guiding treat-ment decisions. While conventional systems exist, they often lack precision and struggle to handle complex, multidimensional data with limited resource availability. This study investigates the potential use of machine learning (ML) techniques to enhance in-hospital mortality prediction in ICU patients with heart failure (HF). An ML-based approach has been proposed in this study utilizing electronic health records of ICU patients containing demographic, physiological, laboratory, and medication data. The significance of the features in the dataset has also been evaluated through a voting ensemble technique aggregating the results from multiple feature selection techniques. Various ML classification algorithms are trained,…