Out-of-Hospital Cardiac Arrest Prediction with ECG Features and Shallow Machine Learning Models

This paper explores the development of predictive models aimed at improving the detection of out-of-hospital cardiac arrest (OHCA) using electrocardiogram (ECG) data and shallow machine learning techniques. Publicly available datasets, including ECG data before sudden cardiac arrest (SCA), are used to evaluate the performance of selected machine learning algorithms, such as Decision Trees, Random Forests, and Support Vector Machines (SVM), in predicting OHCA. It was found that SVM with a radial basis function kernel outperformed other models with an average accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.938 \pm$</tex> 0.01. This paper presents the development and testing of this and other selected machine learning models, along with the recommendations for future work.