Clinical Decision Support System to Managing Beds in ICU

This work proposes an AI-based machine learning model using the supervised learning algorithm and a decision tree that allows analyzing and predicting the degree of affectation in patients for their corresponding hospitalization in a Coronary Intensive Care Unit (CICU). The methodology used follows the scientific method that allowed us to investigate and examine all kinds of studies, results and research. The dataset used was obtained from the repository, coming from a hospital specialized in cardiovascular diseases. The total performance of the model reaches 79.27% of accuracy. With these f-score it is possible to determine the state of gravity of a patient and the action to take according to their state.