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Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events
Michael Schrempf, Diether Kramer, Stefanie Jauk, Sai P. K. Veeranki, Werner Leodolter, Peter P. Rainer
136 - 143
10.3233/SHTI210100
Research Article
Studies in Health Technology and Informatics
Volume 279: Navigating Healthcare Through Challenging Times
Abstract

Background:

Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented with the right interventions.

Objectives:

The aim of this study was to develop machine learning-based models for the 5-year risk prediction of MACE.

Methods:

The data used for modelling included electronic medical records of more than 128,000 patients including 29,262 patients with MACE. A feature selection based on filter and embedded methods resulted in 826 features for modelling. Different machine learning methods were used for modelling on the training data.

Results:

A random forest model achieved the best calibration and discriminative performance on a separate test data set with an AUROC of 0.88.

Conclusion:

The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.

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