A Case Study on MIMIC-III Dataset: Comparing the Function of Different Classifiers

MIMIC-III (Medical Information Mart for Intensive Care III) is a large, freely-available database comprising of 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. This database includes information such as demographics, vital sign measurements made at the bedside, laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality and classification of these data is an important problem. In this paper, we used different types of classification models like Random Forest, KNN, Logistic Regression, Etc. Moreover, we have reached F1 score of 93.5% in Gradient Boosting Classifier with the base learner of Random Forest.