An Integration of Cardiovascular Event Data and Machine Learning Models for Cardiac Arrest Predictions

Abstract

Purpose: Predicting and then preventing the cardiac arrest of a patient in ICU is the most challenging phase even for a most highly skilled professional. The data have been collected in ICU for a patient are huge, and the selection of a portion of data for preventing cardiac arrest in a quantum of time is highly decisive, analysing and predicting that large data require an effective system. An effective integration of computer applications and cardiovascular data is necessary to predict cardiovascular risks. A machine learning technique is a right choice in the advent of technology to manage patients with cardiac arrest. Methodology: In this work, we have collected and merged three data sets, Cleveland Dataset of US patients with total 303 records, Starlog Dataset of UK patients with 270 records, and Hungarian dataset of Hungary, Switzerland with 617 records. These data are the most comprehensive data set with a combination of all three data sets consisting of 11 common features with 1190 records. Findings/Results: Feature extraction phase extracts 7 features, which contribute to the event. In addition, extracted features are used to train the selected machine learning classifier models, and results are obtained, and obtained results are then evaluated using test data and final results are drawn. Extra Tree Classifier has the highest value of 0.957 for the average area under the curve (AUC). Originality: The originality of this combined Dataset analysis using machine learning classifier model results from Extra Tree Classifier with the highest value of 0.957 for the average area under the curve (AUC).