Sistema inteligente para la predicción de mortalidad en Unidades de Cuidados Intensivos

María del Rocío Vicente González; María Matilde  García Lorenzo; Rafael Fernández Fleites; Roberto Vicente Rodríguez

Authors

María del Rocío Vicente González Universidad Central "Martha Abreu" de Las Villas https://orcid.org/0009-0007-8841-0950
María Matilde García Lorenzo Universidad Central "Martha Abreu" de Las Villas
Rafael Fernández Fleites Universidad Central "Martha Abreu" de Las Villas
Roberto Vicente Rodríguez Universidad Central "Martha Abreu" de Las Villas

Keywords:

Intensive Care Unit, Multilayer Perceptron, LIME, Mortality prediction, Explainable artificial intelligence

Abstract

Early mortality prediction in Intensive Care Units (ICU) using machine learning techniques has become a key tool for supporting clinical decision-making in highly dynamic and critical healthcare environments. In this study, a Multilayer Perceptron (MLP) neural network model was developed to predict mortality in the ICU of the "Arnaldo Milián Castro" University Hospital in Villa Clara, Cuba. Preprocessing techniques were applied to handle inconsistencies in clinical data, along with balancing methods such as BorderlineSMOTE to address class imbalance. The model achieved notable metrics in field tests: sensitivity above 85%, area under the ROC curve (AUC-ROC) of 0.93, and high overall accuracy. Additionally, the LIME method (Local Interpretable Model-Agnostic Explanations) was implemented to enhance the interpretability of predictions. The results demonstrate the system's potential to identify at-risk patients with minimal error margin, providing a valuable support tool for medical staff in intensive care settings.

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