In this project, I develop predictive models to assess the mortality risk of patients admitted to Intensive Care Units (ICUs). I utilize two machine learning algorithms: K-Nearest Neighbors (KNN) and Support Vector Machines (SVM). Through cross-validation, I achieved an Area Under the Receiver Operating Characteristic Curve (AUC) score of 92.3% with KNN and 93.2% with SVM, signifying a high level of accuracy in mortality prediction.
The dataset comes from the MIMIC project. MIMIC-III (Medical Information Mart for Intensive Care III) is a large, freely-available database comprising de-identified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012.